Genetic algorithms with shrinking population size


 Ada Carter
 2 years ago
 Views:
Transcription
1 Comput Stat (2010) 25: DOI /s ORIGINAL PAPER Genetic algorithms with shrinking population size Joshua W. Hallam Olcay Akman Füsun Akman Received: 31 July 2008 / Accepted: 7 April 2010 / Published online: 22 April 2010 SpringerVerlag 2010 Abstract A Genetic Algorithm (GA) is an evolutionary computation technique inspired by the principle of biological evolution via natural selection. It employs the fundamental components of evolution, such as selection, mating, and mutation, which continue from generation to generation, creating better solutions as time progresses. Although it is mostly used as an optimization tool, GA enjoys a wide spectrum of applications in diverse fields such as engineering, medicine, and ecology, among others. In this study, we propose three different population size reduction methods for a typical GA optimization, aiming to increase efficiency. Additionally, we compare the accuracy and precision of these methods using Monte Carlo simulations. Keywords Genetic algorithms Population reduction Adaptive Exponential Linear reduction 1 Introduction A genetic algorithm is an optimization technique inspired by biological evolution operating under natural selection. First popularized by Holland (1975) and extensively studied by Goldberg (1989), this technique has been shown to be robust, and capable of dealing with highly multimodal and discontinuous search landscapes, where the traditional optimization techniques fail. Traditional methods such as hillclimbing Supported by program of excellence award from Illinois State University. J. W. Hallam O. Akman (B) F. Akman Department of Mathematics, Illinois State University, Campus Box 4520, Normal, IL , USA
2 692 J. W. Hallam et al. and derivativebased methods are able to find optimal points, but with multimodal landscapes, they may get stuck in local optima, whereas the structure of genetic algorithms help avoid this problem. In genetic algorithms, a group of possible solutions, i.e., a population of chromosomes in genetic algorithm terminology, are evaluated and given a fitness value based on this evaluation. The chromosomes with large fitness values are allowed to mate with other chromosomes, mutate, and move on to the next generation. This process is repeated until either a certain number of generations are reached or there is no change in the best solution found for many generations. At the end of the algorithm, the chromosome with the highest fitness is considered to be the solution. In order to take advantage of the process inspired by evolution and natural selection, chromosomes are encoded using a binary string. Let l denote the length of the string. Typically, if the function being optimized has n independent variables, then l is an integer multiple of n. The binary string is then broken into equal parts of length n, each representing one of the n variables, and converted into a real number based on the range of possible values for the variable. The fitness associated with the chromosome is calculated by evaluating the function being optimized at the n real values for each of the variables. More formally, if f : R n R denotes the function being optimized, and g :{0, 1} l R n gives the transformation from the binary string to the real values, then the fitness of a chromosome is calculated as fitness = f (g(chrom)), and the chromosomes with large fitness are chosen for the next generation. However, this choice is not deterministic. Instead, two chromosomes are selected at random, the one with the higher fitness is chosen, and the other is put back. This technique is called binary tournament. Chromosomes can be chosen more than once in a tournament. The chosen chromosomes are then put in the mating pool. This process continues until the mating pool reaches the size of the population in the next generation. Then two chromosomes are chosen from the pool and mated. This mating is analogous to genetic recombination, in which segments of the code are swapped between the two chromosomes. The number of crossover points is up to the user, but in our work we used three. After the mating occurs, the two new chromosomes are mutated. With a certain small probability, each bit may be changed from 0 to 1 or 1 to 0. This process of crossover and mutation creates two new chromosomes, which will be put into the next generation s population. This continues until all pairs in the mating pool are mated. The process of selection, mating, and mutation continue from generation to generation, creating better solutions as time progresses. In a typical genetic algorithm, the population size remains constant throughout the entire algorithm. The conditions under which the theoretical convergence (when the number of generations tends to infinity) is assured for constant size genetic algorithm are in Bhandari et al. (1996). In this study, we propose a genetic algorithm that reduces population size at every time step. This reduction initially allows for a larger population size. With a larger size, the genetic pool is more expansive, and the algorithm has a better chance of selecting parts of the correct solution early. Additionally, the reduction is controlled by userdefined parameters which do not allow the population to be reduced drastically, to avoid being trapped in local optima. We believe that reducing population size will enable the algorithm to find the correct solution more efficiently.
3 Genetic algorithms with shrinking population size 693 In Sect. 2 we study different methods of population reduction, while in Sect. 3, we examine the efficiency of these methods. Section 4 contains simulation results, followed by a discussion in Sect Methods of population reduction We have developed three different methods of population reduction for genetic algorithms. The first is an adaptive measure and the other two are based on a predetermined pattern. We describe these three methods in detail below. 2.1 Adaptive population reduction Adaptively sizing population is defined as continually changing the population size based on parameters within the algorithm. These changes could include those in average fitness and genetic variance. This method contrasts with predetermined sizing methods, in which the population size at each generation is unaffected by changes in the algorithm. Adaptive measures have been offered by several authors and a review of current methods can be found in Lima and Lobo (2005). Here we present a new method based on the change in best fitness. This approach was used before by Eiben et al. (2004). Their method was to increase the population size if the best fitness increases, decrease the size if there is a short term lack of fitness increase, and increase the population size if no change occurs for a long period of time. This approach may have several problems with it. For example, if the population is increased, then new chromosomes must be created. However, if the chromosomes are just created by cloning existing chromosomes, then there has not been an increase in genetic diversity. In the Eiben et al. study (2004) the best individuals were cloned, which did not increase genetic diversity. It would be more beneficial, in theory, to generate random individuals to simulate gene flow. Another problem is that typically in a genetic algorithm the fitness increases the quickest early in the algorithm, which would imply that the population size grows early in the algorithm. If the individuals are cloned, then the population will lose genetic diversity even faster because of the dominance of the numerous clones with large fitness. It seems, if the population size will likely increase early in the algorithm, that simply starting with a larger randomly generated population and not increasing population size would be better because of higher genetic diversity, and the same amount of computation would be used. Our approach takes the opposite view to Eiben et al. (2004). We believe that as the best fitness increases we may reduce the population size and obtain good results with less computation than a typical genetic algorithm. The only time population size is reduced is when the best fitness increases, and the method never increases the population size. To justify this, suppose we wish to optimize in a multimodal fitness landscape. If we start with a large population, then we can better explore this large landscape. However, as time continues, solutions will aggregate around a certain area in the landscape, and we can reduce the population size. Since the chromosome with the best fitness will be allowed to mate often, the solutions will concentrate around this solution. Thus, the change in best fitness is a good indicator of how well the
4 694 J. W. Hallam et al. algorithm is performing. The initial landscape is typically rugged and we need many chromosomes to explore it, but as the algorithm continues to run we can think of the problem as shrinking in ruggedness since we are concentrating on a smaller section of the landscape. A reduction in ruggedness allows for a smaller population to optimize the problem with the same or better results than a larger population. The small population size, with implementation of elitism, allows genetic drift to finetune the solution without losing the best solution in the process: suppose the population has aggregated in a small partition of the search space such that there are only slight changes in fitness. At this point, it is economical to have a small population, because a chromosome with a small difference in fitness has a better chance to be chosen to participate in a tournament. Although the choice to participate in the tournament is random, with a smaller population, every chromosome has a better chance to be chosen. Thus, those with a slightly better fitness can participate and be chosen for the mating pool. At the same time, this part of the algorithm is merely choosing between solutions which only differ little and it is less important than the phase of the algorithm making large jumps in fitness. We have developed a formula to quantify the amount of reduction. It is based on the idea that the population size should be reduced proportionally to the change in best fitness. Let N t be the population size at generation t. Denote the change in best fitness at generation t by ft best = ( ft 1 best f t 2 best ) / f best t 2. We use the absolute value to deal with fitness values which can be both positive and negative. We then determine a parameter fmax best such that ( ) 1 f best t Nt, if ft best fmax best ( ) N t+1 = 1 f best max Nt, if ft best > fmax best MIN_POPSIZE, if N t+1 is less than MIN_POPSIZE. As seen in Eq. 1, the size of the population of the next generation depends on several factors. The first is the percent change in best fitness ( ft best ). If this is below some threshold value ( fmax best ), then the population of the next generation is reduced by this percent change. If it is more, then the population is reduced by the threshold value. If either of these reductions reduces the size so that it is below the minimum size, then the size of the population is set to be the minimum size. When this type of decrease is used, we implement elitism, allowing the best chromosome to continue to the next generation without change, so that the change in fitness is always nonnegative. Clearly, we have fmax best < 1, since if f best max 1 then the population will be immediately reduced to the minimum size. As a side note, the typical genetic algorithm is a special case of the method we have produced, where fmax best = 0. The determination of minimum population size is arbitrary. However, to avoid the negative effects of extremely small populations, we set MIN_POPSIZE = 20 based on work by Reeves (1993). As can be seen from Eq. 1 and Fig. 1, the shape of the population curve is exponential decrease, followed by a steady section, again followed by an exponential decrease, and this pattern continues. In Fig. 1, the exact solution was not found before the 200th generation for Rastrigin s function and Rosenbrock s valley function. Rastrigin s function is a multimodal function which, in our experiments, (1)
5 Genetic algorithms with shrinking population size 695 Fig. 1 Population size for the adaptive method for the first 200 generations of several functions. Boxed in areas denote the generation where actual solution was found. The exact solution was not found in Rastrigin s function and Rosenbrock s valley function within 200 generations tended to need many generations to converge to the exact solution. Rosenbrock s valley function is unimodal, but very flat, requiring many generations to converge. 2.2 Predetermined exponential decrease Although the adaptive method produces a population curve which has segments of exponential decrease, it requires computing ft best at every generation, as well as the determination of fmax best. We now present a method which requires neither and reduces the population exponentially. Many theoretical results concerning genetic algorithms rest on Holland s Schema Theorem (Holland 1975). A schema can be thought of as parts of solutions (i.e., parts of a binary string) that come together to form more fit chromosomes. Holland was able to show that short schema with high fitness tend to increase in the population exponentially by crossover. This exponential increase brings highly fit segments of strings together to form better and better solutions. The Schema Theorem really underscores the importance of crossover, a characteristic of genetic algorithms that distinguishes it from other methods such as hillclimbing. Based on this theorem, we believe that we can reduce the population size exponentially and obtain results comparable to an algorithm which has no reduction. To perform this reduction, the following formula
6 696 J. W. Hallam et al. is used: N t = (N 0 + α)e c t α, wherec = ln N END α N 0 +α number of generations. (2) Here α is a parameter used to shape the population curve. In all of our studies α = 5 was chosen based on empirical evidence. Also, N END denotes the population size at the end of the algorithm. It is set to be 20, in agreement with the minimum population size used in the adaptive method. 2.3 Predetermined linear decrease It is not possible to predict the shape of the exponential increase of schema without direct and complicated calculations during the algorithm. Therefore, we have also developed a reduction method which is not exponential, but instead, which decreases the population size in a linear trend. This avoids decreasing the population too quickly, but has the benefit of reducing the number of computations needed in a traditional genetic algorithm. The following formula is used to determine the population size at each generation: N t = mt + N 0, where m = N 0 N END number of generations. (3) Again, we set N END = 20. Figure 2 depicts all three methods given above for population reduction and the typical (no reduction) method. 3 Testing reduction methods To determine the effectiveness of the reduction methods, we ran simulations using five sets of problems. Three of the five sets were based on massively multimodal functions and the others on unimodal functions. The three multimodal functions have different properties that make them interesting to compare. Although many real world problems for which genetic algorithms work well are usually multimodal, we chose to use two unimodal functions as well, in order to understand how our new reduction methods are affected by the shape of the fitness landscape. All five of the functions we used can be defined for an arbitrary number of independent variables. In all of the simulations each function had 2 10 variables. Tables 1 and 2 give information about the test functions in which n is the total number of independent variables and x j is the jth independent variable. 3.1 Fixed number of computations Two different types of simulations were run in testing the three new populationreduction techniques. In the first type the number of total computations was fixed.
7 Genetic algorithms with shrinking population size 697 Fig. 2 Population size for the exponential, linear, and adaptive reduction methods and the typical GA (no reduction) Table 1 Test functions and their equations Name Equation Type Sphere model ni=1 xi 2 Unimodal Rastrigin s function 10n + n i=1 (xi 2 10 cos (2π x i )) Multimodal ( Rosenbrock s valley function ni=2 100(x i xi 1 2 )2 + (1 x i 1 ) 2) Unimodal Schwefel s function ni=1 x i (sin x i ) Multimodal ( ni=1 ) x Ackley s path function 20 exp 0.2 i 2 n Multimodal ( ni=1 ) cos (2π x exp i ) n e (A computation refers to a single call to the function being optimized.) Using binary tournament, the number of function calls can be approximated by the number of chromosomes that will continue to the next generation. This is only an approximation, because if a chromosome is chosen more than once for the tournament, then its fitness does not need to be recalculated. After a fixed number of computations were performed, we determined the absolute difference between the best solution given by that run of the algorithm and the actual solution. For each simulation, 100 iterations
8 698 J. W. Hallam et al. Table 2 Test functions and their parameters Name Domain of x i Optimal values x Minimum Sphere model [ 6,6] (0,0,,0) 0 Rastrigin s function [ 6,6] (0,0,,0) 0 Rosenbrock s valley function [ 2.048,2.048] (1,1,,1) 0 Schwefel s function [ 500,500] ( , ,, ) n* Ackley s path function [ ,32.768] (0,0,,0) 0 Table 3 Number of computations allowed based on the number of independent variables Number of variables 2 10, , , , , , , , ,000 Number of computations allowed of the algorithm were run for the three different reduction methods in addition to the traditional genetic algorithm. Using the 100 solutions of each type, we performed t tests to determine if any of the three new methods outperformed the traditional genetic algorithm. Also, we looked at the number of times the algorithm found the correct solution. We used two different types of functions to be optimized, each with a number of variables ranging from 2 to 10. As the number of variables increases, the difficulty in finding the optimal solution increases. In light of this, as the number of variables increased, we allowed more computations to be performed. Table 3 gives the number of computations allowed for a given number of variables. 3.2 Fixed solution with acceptable tolerance With the second type of simulation we performed, the number of computations was not fixed; instead, we let the algorithm run until the solution it gave was in a certain radius about the actual solution. Once the best solution was in the tolerance level, the algorithm terminated, and the number of computations was recorded. Only Rastrigin s function and the sphere model were used for this simulation. Again, 100 iterations of each method were used, and a ttest was performed to determine whether there was a significant difference in the number of computations, required by each method to be within the tolerance level. In the adaptive and typical methods, the choice of number of generations was arbitrary. This was not the case for the exponential and linear decrease,
9 Genetic algorithms with shrinking population size 699 Table 4 Initial population size (number of generations) for fixed number of computations simulation Number of variables Adaptive Exponential Linear Typical (500) 100 (250) 100 (250) 100 (100) (500) 100 (250) 100 (250) 100 (125) (500) 100 (250) 100 (250) 100 (150) (500) 150 (250) 100 (250) 150 (175) (500) 200 (250) 100 (250) 200 (200) (500) 250 (250) 100 (250) 250 (250) (750) 300 (300) 100 (300) 300 (300) (750) 300 (350) 100 (350) 300 (350) (750) 300 (400) 100 (400) 300 (400) Table 5 Initial population size (number of generations) for tolerance study using Rastrigin s function and sphere model respectively Number of variables Adaptive Exponential Linear Typical 2,3,4, ( ) ( ) ( ) ( ) 6,7,8,9, ( ) ( ) ( ) ( ) as their population curves depend on the total number of generations. When choosing the number of generations, we erred on the side of more computation. In effect, we set the number of generations higher than necessary, which caused the population curve to have a less steep decline. This may have skewed results slightly in the direction of overcomputation, but we feel that this outweighed the problem of an algorithm not reaching the tolerance level. 3.3 Simulation parameters In all the simulation runs, the probability of mutation was set at The binary string length was 15 bits per independent variable. The number of crossover points was 3 and each crossover point was chosen so that it occurred at a multiple of 15. For the adaptive reduction method, we set fmax best =.08. For the fixed tolerance level simulation, the tolerance level was.05. Table 4 gives the initial population size and the number of generations for the fixed number of computations simulation. Table 5 gives the same information for the fixed tolerance simulation. In both types of simulations, outliers were removed from the dataset before the ttests were done. 4 Results 4.1 Fixed number of computations It is clear from the mean distance results in Table 6 and Figs. 3 and 4 that the adaptive reduction method performs well for a wide range of functions. It was never
10 700 J. W. Hallam et al. Table 6 Average distance from actual solution after outliers were removed Sphere Adaptive 6.71E E E E E E 7* 2.68E 7* 3.02E 7* 3.35E 7* Exponential 6.71E E E E E E E 6* 1.03E 5* 3.47E 5* Linear 6.71E E E E E 6* 1.33E 5* 8.55E 5* 1.90E 4* 4.05E 4* Typical 6.71E E E E E 7 3.5E E E E 5 Rastrigin s Adaptive 1.33E 5* 2E 5* 2.66E 5* 3.75E 5* 3.25E 1* 4.17E 1* 3.26E 1* 4.47E 1* 6.36E 1* Exponential 3.20E E E E E E E 1* 6.36E 1* 9.83E 1 Linear 3.55E E 1* 5.44E E E 1* 4.56E E Typical 3.84E E E E E E E Rosenbrock s Adaptive 5.30E 3* 1.87E 1* 7.06E 1* 1.59* 2.49* 3.50* Exponential 2.32E 2* 2.97E 1* * 4.27* Linear 2.53E 2* 4.17E * * Typical 1.47E E Schwefel s Adaptive 1.52E 3* 3.45E 2* 6.99E 2* 1.27E 1* 1.64E 1* 2.33E 1* 5.98E 1* 8.53E 1* 1.15* Exponential 8.63E E E E E 1* 2.49E 1* 2.61E 1* 3.33E 1* 4.98E 1* Linear 7.35E E E E E 1* 2.30E 1* 3.09E 1* 4.16E 1* 6.31E 1* Typical 8.21E E E E E E+1 Ackley s Adaptive 4.05E E E E E E 3* 4.46E 3* 4.05E 3* 4.05E 3* Exp 4.05E E E E E E 3* 1.36E 2* 2.00E 2* 3.15E 2* Linear 4.05E E E E E 3* 2.43E 2* 6.95E 2* 9.96E 2* 1.32E 1* Typical 4.05E E E E E E E E E 2 * denotes significantly better than the typical genetic algorithm * denotes significantly worse than the typical genetic algorithm, both at the α =.05 level outperformed by the typical genetic algorithm, and it outperformed the typical genetic algorithm in 34 out of the 45 total categories. This would imply that the adaptive method is robust and could be used on a number of functions with no loss of efficiency and usually with better performance. The predetermined exponential decreasing method was the next best, outperforming 11 times and being outperformed seven times. Finally, the linear reduction method was outperformed 12 times, and it outperformed the typical method nine times. Table 7 gives the fraction of replications that found the correct solution for each function with different number of variables and the average over all variables. In the table, the results for Rosenbrock s valley function were excluded because only once the correct solution was found. Again the results indicate that the adaptive reduction method is the preferred method. It had the largest average frequency of exact hits for three of the four functions considered.
11 Genetic algorithms with shrinking population size 701 Fig. 3 Results for fixed number of computations with 95% CI 4.2 Fixed solution with acceptable tolerance We turn our attention to the simulation in which the number of computations was not fixed, but the tolerance about the solution was fixed (See Table 8 for results). To reiterate, the algorithm ran until it gave a solution that was in the interval of [ 0.05,0.05] around the solution. As with the previous simulation, the adaptive reduction method was the best, finding the neighborhood with a significantly less amount of computation than the typical method in 17 out of the 18 categories. Additionally, the exponential decrease method came in second, outperforming eight times and being outperformed
12 702 J. W. Hallam et al. Fig. 4 Results for fixed number of computations with 95% CI. The legend is the same as in Fig. 3
13 Genetic algorithms with shrinking population size 703 Table 7 The proportion of replications in which the genetic algorithm got the correct solution for different number of variables Average Sphere Adapt Exp Linear Typical Rastrigin s Adapt Exp Linear Typical Schwefel s Adapt Exp Linear Typical Ackley s Adapt Exp Linear Typical The data for Rosenbrock s valley function was omitted because only one of the 3,600 replications found the correct solution four times. The linear decrease method once again did the worst among the reduction methods, outperforming only six times and being outperformed four times. It is interesting to note that with Rastrigin s function and the sphere model, once the algorithm has reached the.05 neighborhood, it is likely that the algorithm will find the correct solution as there is only one minimum in the.05 neighborhood. This is not true for any of the other test functions used in the first simulation. This is why this simulation was run with these two functions. With Rastrigin s function, the average number of computations sometimes exceeded the number of computations allowed for the first type of simulation. In the first simulation, not all the algorithms found the correct solution, supporting the concept that once in the neighborhood the correct solution would be found. 5 Discussion It is clear that the adaptive method was the best method of reduction, outperforming the typical method in almost all the simulations. When we first started the study, we hypothesized that the adaptive method would work well for complex functions. This
14 704 J. W. Hallam et al. Table 8 The average number of computations needed to get into the [.05,.05] neighborhood about the actual solution Sphere Adapt * 1272* 1510* 1736* 1941* 2184* 2401* 2671* Exp * 2163* * 3829* 4487* 5181* Linear * * 4907* 5664* Typical Rastrigin s Adapt 4295* 7810* 13667* 14532* 19673* 23801* 27951* 31944* 39837* Exp * 14127* * * * * Linear * 14747* * * * * Typical * denotes significantly better than the typical genetic algorithm * denotes significantly worse than the typical genetic algorithm, both at the α = 0.05 level is true, but it also did well even for less complex problems. Arguably, the adaptive method should be preferred over a typical genetic algorithm. There are two drawbacks to the adaptive method: one is unique to the adaptive method, and the other is a general problem with population reduction techniques. The first is that the parameter fmax best has to be determined aprioriand usually ad hoc. There are some guidelines that can be given. First, if some information about the search space is known, and the user knows that large jumps in the fitness values are possible, then a lower value should be chosen. This prevents the population from reaching its minimum size too quickly. For example, if the functions f 1 (x) = 100x 2 and f 2 (x) = x 2 were being optimized, we would have f1max best < fbest 2max. Similarly, if the search space is relatively flat, then the parameter should be larger. This is most likely why the adaptive reduction method did not do that well on Rosenbrock s valley function. The search space is quite flat and the changes in fitness are very small, which made the population decrease slowly. In turn, this increased the number of computations quickly from generation to generation, and the algorithm had to terminate at an early generation because it hit the maximum number of computations. The second drawback to the adaptive method, actually to any reduction method, is that usually the initial size is chosen to be larger, and is then reduced to compare it to a typical genetic algorithm with the same number of computations, but with a steady population size. This comparison is fair for a serial implementation of a genetic algorithm, but for a parallel implementation, it may not be. Because most time is spent evaluating the fitness function, parallel implementation can be used to evaluate the chromosomes separately. This can reduce the long computation time caused by the bottleneck of fitness evaluation. However, with a shrinking population that starts with a larger initial size, parallel implementation may not be as beneficial. This is true as long as the number of independent processors is larger than the population size in the typical genetic algorithm. If the number of processors is less than or equal to the minimum number of chromosomes in a shrinking population, then
15 Genetic algorithms with shrinking population size 705 the parallel implementation for a typical genetic algorithm should not be significantly better than that of a population reducing genetic algorithm. The other two methods, exponential and linear decrease, did not do as well as the adaptive reduction method. Possibly this occurred because they reduce the population too slowly, which prevents a large initial size. The large initial size may have been the key factor in the ability of the adaptive method to do so well. Although these tests were done for a genetic algorithm with the same features as a typical genetic algorithm except for the changing population size, there is no reason to believe that the reduction techniques will not work well for genetic algorithms with other modifications. For example, a genetic algorithm with niching may work well with the adaptive reduction technique. In this scenario, each subpopulation could be reduced independently of the other subpopulations, or reduction may depend on the evolution of the entire system. References Bhandari D, Murthy CA, Pal SK (1996) Genetic algorithm with elitist model and its convergence. Intern J Pattern Recognit Artif Intell 10: Eiben AE, Marchiori E, Valkó VA (2004) Evolutionary algorithms with onthefly population size adjustment. In: Yao X et al (ed) Parallel problem solving from nature, PPSN VIII, Lecture notes in computer science, vol Springer, Berlin, pp Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. AddisonWesley, Reading Holland J (1975) Adaptation in natural and artificial systems. The MIT Press, Ann Arbor Lima CF, Lobo FG (2005) A review of adaptive population sizing schemes in genetic algorithms. In: Proceedings of the 2005 workshops on genetic and evolutionary computation. ACM, New York, pp Reeves CR (1993) Using genetic algorithms with small populations. In: Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann Publishers, San Francisco, pp 92 97
Evolutionary Computation
Evolutionary Computation BIOINSPIRED OPTIMIZATION TECHNIQUES IN PARALLEL AND DISTRIBUTED SYSTEMS Inspiration Darwin Transmutation of species Evolution Origin of species 1809 Altering of one species into
More informationGenetic Algorithms. What is Evolutionary Computation? The Argument. 22c: 145, Chapter 4.3
Genetic Algorithms 22c: 145, Chapter 4.3 What is Evolutionary Computation? An abstraction from the theory of biological evolution that is used to create optimization procedures or methodologies, usually
More informationIntroduction To Genetic Algorithms
1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization
More informationGenetic Algorithms and Evolutionary Computation
Genetic Algorithms and Evolutionary Computation Matteo Matteucci and Andrea Bonarini {matteucci,bonarini}@elet.polimi.it Department of Electronics and Information Politecnico di Milano Genetic Algorithms
More informationNonUniform Mapping in BinaryCoded Genetic Algorithms
NonUniform Mapping in BinaryCoded Genetic Algorithms Kalyanmoy Deb, Yashesh D. Dhebar, and N. V. R. Pavan Kanpur Genetic Algorithms Laboratory (KanGAL) Indian Institute of Technology Kanpur PIN 208016,
More information11/14/2010 Intelligent Systems and Soft Computing 1
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationLecture 9 Evolutionary Computation: Genetic algorithms
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationOptimizing CPU Scheduling Problem using Genetic Algorithms
Optimizing CPU Scheduling Problem using Genetic Algorithms Anu Taneja Amit Kumar Computer Science Department Hindu College of Engineering, Sonepat (MDU) anutaneja16@gmail.com amitkumar.cs08@pec.edu.in
More informationHolland s GA Schema Theorem
Holland s GA Schema Theorem v Objective provide a formal model for the effectiveness of the GA search process. v In the following we will first approach the problem through the framework formalized by
More informationA Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. AlAmin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
More informationIntroduction to Evolutionary Computation
Introduction to Evolutionary Computation Patrick Reed Department of Civil and Environmental Engineering The Pennsylvania State University preed@engr.psu.edu Slide 1 Outline What is Evolutionary Computation?
More informationNumerical Research on Distributed Genetic Algorithm with Redundant
Numerical Research on Distributed Genetic Algorithm with Redundant Binary Number 1 Sayori Seto, 2 Akinori Kanasugi 1,2 Graduate School of Engineering, Tokyo Denki University, Japan 10kme41@ms.dendai.ac.jp,
More informationthat simple hillclimbing schemes would perform poorly because a large number of bit positions must be optimized simultaneously in order to move from
B.2.7.5: Fitness Landscapes: Royal Road Functions Melanie Mitchell Santa Fe Institute 1399 Hyde Park Road Santa Fe, NM 87501 mm@santafe.edu Stephanie Forrest Dept. of Computer Science University of New
More informationQuad Search and Hybrid Genetic Algorithms
Quad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and JeanPaul Watson whitley,garrett,watsonj @cs.colostate.edu Department of Computer Science, Colorado State University Fort Collins,
More informationGenetic Algorithm an Approach to Solve Global Optimization Problems
Genetic Algorithm an Approach to Solve Global Optimization Problems PRATIBHA BAJPAI Amity Institute of Information Technology, Amity University, Lucknow, Uttar Pradesh, India, pratibha_bajpai@rediffmail.com
More informationEvolutionary Computation
Evolutionary Computation Cover evolutionary computation theory and paradigms Emphasize use of EC to solve practical problems Compare with other techniques  see how EC fits in with other approaches Definition:
More informationThe Influence of Binary Representations of Integers on the Performance of Selectorecombinative Genetic Algorithms
The Influence of Binary Representations of Integers on the Performance of Selectorecombinative Genetic Algorithms Franz Rothlauf Working Paper 1/2002 February 2002 Working Papers in Information Systems
More informationAn introduction to evolutionary computation
An introduction to evolutionary computation Andrea Roli Alma Mater Studiorum Università di Bologna Cesena campus andrea.roli@unibo.it Inspiring principle Evolutionary Computation is inspired by natural
More informationAlpha Cut based Novel Selection for Genetic Algorithm
Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that can
More informationSolving Timetable Scheduling Problem by Using Genetic Algorithms
Solving Timetable Scheduling Problem by Using Genetic Algorithms Branimir Sigl, Marin Golub, Vedran Mornar Faculty of Electrical Engineering and Computing, University of Zagreb Unska 3, 1 Zagreb, Croatia
More informationF. Greene 6920 Roosevelt NE #126 Seattle, WA
Effects of Diploid/Dominance on Stationary Genetic Search Proceedings of the Fifth Annual Conference on Evolutionary Programming. San Diego: MIT Press. F. Greene 6920 Roosevelt NE #126 Seattle, WA 98115
More informationEstimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects
Journal of Computer Science 2 (2): 118123, 2006 ISSN 15493636 2006 Science Publications Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects Alaa F. Sheta Computers
More informationGenetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm
24 Genetic Algorithm Based on Darwinian Paradigm Reproduction Competition Survive Selection Intrinsically a robust search and optimization mechanism Slide 47  Conceptual Algorithm Slide 48  25 Genetic
More informationComparison of Major Domination Schemes for Diploid Binary Genetic Algorithms in Dynamic Environments
Comparison of Maor Domination Schemes for Diploid Binary Genetic Algorithms in Dynamic Environments A. Sima UYAR and A. Emre HARMANCI Istanbul Technical University Computer Engineering Department Maslak
More informationProgramming Risk Assessment Models for Online Security Evaluation Systems
Programming Risk Assessment Models for Online Security Evaluation Systems Ajith Abraham 1, Crina Grosan 12, Vaclav Snasel 13 1 Machine Intelligence Research Labs, MIR Labs, http://www.mirlabs.org 2 BabesBolyai
More informationA Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number
A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number 1 Tomohiro KAMIMURA, 2 Akinori KANASUGI 1 Department of Electronics, Tokyo Denki University, 07ee055@ms.dendai.ac.jp
More informationUsing Genetic Algorithms with Asexual Transposition
Using Genetic Algorithms with Asexual Transposition Anabela Simões,, Ernesto Costa Centre for Informatics and Systems of the University of Coimbra,, Polo II, 3030 Coimbra, Portugal Leiria College of Education,
More informationGenetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve
Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods
More informationGenetic Algorithms. Part 2: The Knapsack Problem. Spring 2009 Instructor: Dr. Masoud Yaghini
Genetic Algorithms Part 2: The Knapsack Problem Spring 2009 Instructor: Dr. Masoud Yaghini Outline Genetic Algorithms: Part 2 Problem Definition Representations Fitness Function Handling of Constraints
More informationGenetic algorithms for changing environments
Genetic algorithms for changing environments John J. Grefenstette Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC 375, USA gref@aic.nrl.navy.mil Abstract
More informationA NonLinear Schema Theorem for Genetic Algorithms
A NonLinear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 5042806755 Abstract We generalize Holland
More informationA very brief introduction to genetic algorithms
A very brief introduction to genetic algorithms Radoslav Harman Design of experiments seminar FACULTY OF MATHEMATICS, PHYSICS AND INFORMATICS COMENIUS UNIVERSITY IN BRATISLAVA 25.2.2013 Optimization problems:
More informationGenetic algorithms. Maximise f(x), xi Code every variable using binary string Eg.(00000) (11111)
Genetic algorithms Based on survival of the fittest. Start with population of points. Retain better points Based on natural selection. ( as in genetic processes) Genetic algorithms L u Maximise f(x), xi
More informationCopyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and
Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere
More informationGenetic Algorithms and Sudoku
Genetic Algorithms and Sudoku Dr. John M. Weiss Department of Mathematics and Computer Science South Dakota School of Mines and Technology (SDSM&T) Rapid City, SD 577013995 john.weiss@sdsmt.edu MICS 2009
More informationOriginal Article Efficient Genetic Algorithm on Linear Programming Problem for Fittest Chromosomes
International Archive of Applied Sciences and Technology Volume 3 [2] June 2012: 4757 ISSN: 09764828 Society of Education, India Website: www.soeagra.com/iaast/iaast.htm Original Article Efficient Genetic
More informationEvolutionary Computation: A Unified Approach
Evolutionary Computation: A Unified Approach Kenneth De Jong Computer Science Department George Mason University kdejong@gmu.edu www.cs.gmu.edu/~eclab 1 Historical roots: Evolution Strategies (ESs): developed
More informationLearning in Abstract Memory Schemes for Dynamic Optimization
Fourth International Conference on Natural Computation Learning in Abstract Memory Schemes for Dynamic Optimization Hendrik Richter HTWK Leipzig, Fachbereich Elektrotechnik und Informationstechnik, Institut
More informationMemory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of AlNahrain University Vol.15 (2), June, 2012, pp.161168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College
More informationThe Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem
The Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem Alex Rogers Adam PrügelBennett Image, Speech, and Intelligent Systems Research Group, Department of Electronics and Computer Science,
More informationTh. Bäck Leiden Institute of Advanced Computer Science, Leiden University,The Netherlands
EVOLUTIONARY COMPUTATION Th. Bäck Leiden Institute of Advanced Computer Science, Leiden University,The Netherlands Keywords: adaptation, evolution strategy, evolutionary programming, genetic algorithm,
More informationGenetic Algorithm Evolution of Cellular Automata Rules for Complex Binary Sequence Prediction
Brill Academic Publishers P.O. Box 9000, 2300 PA Leiden, The Netherlands Lecture Series on Computer and Computational Sciences Volume 1, 2005, pp. 16 Genetic Algorithm Evolution of Cellular Automata Rules
More informationModified Version of Roulette Selection for Evolution Algorithms  the Fan Selection
Modified Version of Roulette Selection for Evolution Algorithms  the Fan Selection Adam S lowik, Micha l Bia lko Department of Electronic, Technical University of Koszalin, ul. Śniadeckich 2, 75453 Koszalin,
More informationUsing Genetic Programming to Learn Probability Distributions as Mutation Operators with Evolutionary Programming
Using Genetic Programming to Learn Probability Distributions as Mutation Operators with Evolutionary Programming James Bond, and Harry Potter The University of XXX Abstract. The mutation operator is the
More informationA Hybrid Tabu Search Method for Assembly Line Balancing
Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 1517, 2007 443 A Hybrid Tabu Search Method for Assembly Line Balancing SUPAPORN
More informationEvolutionary SAT Solver (ESS)
Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 35, 2011,
More informationEvolutionary Genetic Algorithms in a Constraint Satisfaction Problem: Puzzle Eternity II
Evolutionary Genetic Algorithms in a Constraint Satisfaction Problem: Puzzle Eternity II Jorge Muñoz, German Gutierrez, and Araceli Sanchis University Carlos III of Madrid Avda. de la Universidad 30, 28911
More informationISSN: 23195967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013
Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm
More informationSelfLearning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints
SelfLearning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints Radomír Perzina, Jaroslav Ramík perzina(ramik)@opf.slu.cz Centre of excellence IT4Innovations Division of the University
More informationGA as a Data Optimization Tool for Predictive Analytics
GA as a Data Optimization Tool for Predictive Analytics Chandra.J 1, Dr.Nachamai.M 2,Dr.Anitha.S.Pillai 3 1Assistant Professor, Department of computer Science, Christ University, Bangalore,India, chandra.j@christunivesity.in
More informationSolving Threeobjective Optimization Problems Using Evolutionary Dynamic Weighted Aggregation: Results and Analysis
Solving Threeobjective Optimization Problems Using Evolutionary Dynamic Weighted Aggregation: Results and Analysis Abstract. In this paper, evolutionary dynamic weighted aggregation methods are generalized
More informationNew Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem
New Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem Radovic, Marija; and Milutinovic, Veljko Abstract One of the algorithms used for solving Traveling Salesman
More informationThe University of Algarve Informatics Laboratory
arxiv:cs/0602055v1 [cs.ne] 15 Feb 2006 The University of Algarve Informatics Laboratory UALGILAB Technical Report No. 200602 February, 2006 Revisiting Evolutionary Algorithms with OntheFly Population
More informationEFFICIENT GENETIC ALGORITHM ON LINEAR PROGRAMMING PROBLEM FOR FITTEST CHROMOSOMES
Volume 3, No. 6, June 2012 Journal of Global Research in Computer Science RESEARCH PAPER Available Online at www.jgrcs.info EFFICIENT GENETIC ALGORITHM ON LINEAR PROGRAMMING PROBLEM FOR FITTEST CHROMOSOMES
More informationAsexual Versus Sexual Reproduction in Genetic Algorithms 1
Asexual Versus Sexual Reproduction in Genetic Algorithms Wendy Ann Deslauriers (wendyd@alumni.princeton.edu) Institute of Cognitive Science,Room 22, Dunton Tower Carleton University, 25 Colonel By Drive
More informationLearning the Dominance in Diploid Genetic Algorithms for Changing Optimization Problems
Learning the Dominance in Diploid Genetic Algorithms for Changing Optimization Problems Shengxiang Yang Abstract Using diploid representation with dominance scheme is one of the approaches developed for
More informationPLAANN as a Classification Tool for Customer Intelligence in Banking
PLAANN as a Classification Tool for Customer Intelligence in Banking EUNITE World Competition in domain of Intelligent Technologies The Research Report Ireneusz Czarnowski and Piotr Jedrzejowicz Department
More informationLevel Sets of Arbitrary Dimension Polynomials with Positive Coefficients and Real Exponents
Level Sets of Arbitrary Dimension Polynomials with Positive Coefficients and Real Exponents Spencer Greenberg April 20, 2006 Abstract In this paper we consider the set of positive points at which a polynomial
More informationKeywords: Travelling Salesman Problem, Map Reduce, Genetic Algorithm. I. INTRODUCTION
ISSN: 23217782 (Online) Impact Factor: 6.047 Volume 4, Issue 6, June 2016 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study
More informationA Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 10
212 International Conference on System Engineering and Modeling (ICSEM 212) IPCSIT vol. 34 (212) (212) IACSIT Press, Singapore A Binary Model on the Basis of Imperialist Competitive Algorithm in Order
More informationLab 4: 26 th March 2012. Exercise 1: Evolutionary algorithms
Lab 4: 26 th March 2012 Exercise 1: Evolutionary algorithms 1. Found a problem where EAs would certainly perform very poorly compared to alternative approaches. Explain why. Suppose that we want to find
More informationHYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM. JinLee KIM 1, M. ASCE
1560 HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM JinLee KIM 1, M. ASCE 1 Assistant Professor, Department of Civil Engineering and Construction
More informationBMOA: Binary Magnetic Optimization Algorithm
International Journal of Machine Learning and Computing Vol. 2 No. 3 June 22 BMOA: Binary Magnetic Optimization Algorithm SeyedAli Mirjalili and Siti Zaiton Mohd Hashim Abstract Recently the behavior of
More informationInertia Weight Strategies in Particle Swarm Optimization
Inertia Weight Strategies in Particle Swarm Optimization 1 J. C. Bansal, 2 P. K. Singh 3 Mukesh Saraswat, 4 Abhishek Verma, 5 Shimpi Singh Jadon, 6,7 Ajith Abraham 1,2,3,4,5 ABVIndian Institute of Information
More informationGenetic Placement Benjamin Kopp Ece556 fall Introduction. Motivation. Genie Specification and Overview
Genetic Placement Benjamin Kopp Ece556 fall 2004 Originally proposed by James P. Cohoon and William D Paris 1987 IEEE Introduction Genetic algorithms are a state space search similar in nature to simulated
More informationSelection Procedures for Module Discovery: Exploring Evolutionary Algorithms for Cognitive Science
Selection Procedures for Module Discovery: Exploring Evolutionary Algorithms for Cognitive Science Janet Wiles (j.wiles@csee.uq.edu.au) Ruth Schulz (ruth@csee.uq.edu.au) Scott Bolland (scottb@csee.uq.edu.au)
More informationA Novel Binary Particle Swarm Optimization
Proceedings of the 5th Mediterranean Conference on T33 A Novel Binary Particle Swarm Optimization Motaba Ahmadieh Khanesar, Member, IEEE, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli K. N. Toosi
More informationEvolutionary Prefetching and Caching in an Independent Storage Units Model
Evolutionary Prefetching and Caching in an Independent Units Model Athena Vakali Department of Informatics Aristotle University of Thessaloniki, Greece Email: avakali@csdauthgr Abstract Modern applications
More informationMarkov chains and Markov Random Fields (MRFs)
Markov chains and Markov Random Fields (MRFs) 1 Why Markov Models We discuss Markov models now. This is the simplest statistical model in which we don t assume that all variables are independent; we assume
More informationGoldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA:
is another objective that the GA could optimize. The approach used here is also adaptable. On any particular project, the designer can congure the GA to focus on optimizing certain constraints (such as
More informationA HYBRID GENETIC ALGORITHM FOR THE MAXIMUM LIKELIHOOD ESTIMATION OF MODELS WITH MULTIPLE EQUILIBRIA: A FIRST REPORT
New Mathematics and Natural Computation Vol. 1, No. 2 (2005) 295 303 c World Scientific Publishing Company A HYBRID GENETIC ALGORITHM FOR THE MAXIMUM LIKELIHOOD ESTIMATION OF MODELS WITH MULTIPLE EQUILIBRIA:
More information(Refer Slide Time: 00:00:56 min)
Numerical Methods and Computation Prof. S.R.K. Iyengar Department of Mathematics Indian Institute of Technology, Delhi Lecture No # 3 Solution of Nonlinear Algebraic Equations (Continued) (Refer Slide
More informationCollege of information technology Department of software
University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/20112012 ***************************************************************************
More informationResearch on a Heuristic GABased Decision Support System for Rice in Heilongjiang Province
Research on a Heuristic GABased Decision Support System for Rice in Heilongjiang Province Ran Cao 1,1, Yushu Yang 1, Wei Guo 1, 1 Engineering college of Northeast Agricultural University, Haerbin, China
More informationA Service Revenueoriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenueoriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationExperimental Design & Methodology Basic lessons in empiricism
Experimental Design & Methodology Basic lessons in empiricism Rafal Kicinger rkicinge@gmu.edu R. Paul Wiegand paul@tesseract.org ECLab George Mason University EClab  Summer Lecture Series p.1 Outline
More informationComparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal
Comparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal Dariusz Król, Tadeusz Lasota 2, Bogdan Trawiński, Krzysztof Trawiński 3, Wrocław University of Technology, Institute of Applied Informatics,
More informationEffect of Using Neural Networks in GABased School Timetabling
Effect of Using Neural Networks in GABased School Timetabling JANIS ZUTERS Department of Computer Science University of Latvia Raina bulv. 19, Riga, LV1050 LATVIA janis.zuters@lu.lv Abstract:  The school
More informationInfluence of the Crossover Operator in the Performance of the Hybrid Taguchi GA
Influence of the Crossover Operator in the Performance of the Hybrid Taguchi GA Stjepan Picek Faculty of Electrical Engineering and Computing Unska 3, Zagreb, Croatia Email: stjepan@computer.org Marin
More informationUsing Segmentbased Genetic Algorithm with Local Search to Find Approximate Solution for MultiStage Supply Chain Network Design Problem
Çankaya University Journal of Science and Engineering Volume 10 (2013), No 2, 185201. Using Segmentbased Genetic Algorithm with Local Search to Find Approximate Solution for MultiStage Supply Chain
More informationCHAPTER 6 GENETIC ALGORITHM OPTIMIZED FUZZY CONTROLLED MOBILE ROBOT
77 CHAPTER 6 GENETIC ALGORITHM OPTIMIZED FUZZY CONTROLLED MOBILE ROBOT 6.1 INTRODUCTION The idea of evolutionary computing was introduced by (Ingo Rechenberg 1971) in his work Evolutionary strategies.
More informationGenetic Algorithm Performance with Different Selection Strategies in Solving TSP
Proceedings of the World Congress on Engineering Vol II WCE, July 68,, London, U.K. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Noraini Mohd Razali, John Geraghty
More informationGENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS
1 GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS STEPHEN D. SLOAN, RAYMOND W. SAW, JAMES J. SLUSS, JR., MONTE P. TULL, AND JOSEPH P. HAVLICEK School of Electrical & Computer Engineering
More informationComparison of algorithms for automated university scheduling
Comparison of algorithms for automated university scheduling Hugo Sandelius Simon Forssell Degree Project in Computer Science, DD143X Supervisor: Pawel Herman Examiner: Örjan Ekeberg CSC, KTH April 29,
More informationA Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms
2009 International Conference on Adaptive and Intelligent Systems A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms Kazuhiro Matsui Dept. of Computer Science
More informationThe effect of population history on the distribution of the Tajima s D statistic
The effect of population history on the distribution of the Tajima s D statistic Deena Schmidt and John Pool May 17, 2002 Abstract The Tajima s D test measures the allele frequency distribution of nucleotide
More informationThe Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy
BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.
More informationPerformance of Hybrid Genetic Algorithms Incorporating Local Search
Performance of Hybrid Genetic Algorithms Incorporating Local Search T. Elmihoub, A. A. Hopgood, L. Nolle and A. Battersby The Nottingham Trent University, School of Computing and Technology, Burton Street,
More informationCOMPARISON OF GENETIC OPERATORS ON A GENERAL GENETIC ALGORITHM PACKAGE HUAWEN XU. Master of Science. Shanghai Jiao Tong University.
COMPARISON OF GENETIC OPERATORS ON A GENERAL GENETIC ALGORITHM PACKAGE By HUAWEN XU Master of Science Shanghai Jiao Tong University Shanghai, China 1999 Submitted to the Faculty of the Graduate College
More informationResource Allocation Schemes for Gang Scheduling
Resource Allocation Schemes for Gang Scheduling B. B. Zhou School of Computing and Mathematics Deakin University Geelong, VIC 327, Australia D. Walsh R. P. Brent Department of Computer Science Australian
More informationFractal Images Compressing by Estimating the Closest Neighborhood with Using of Schema Theory
Journal of Computer Science 6 (5): 591596, 2010 ISSN 15493636 2010 Science Publications Fractal Images Compressing by Estimating the Closest Neighborhood with Using of Schema Theory Mahdi Jampour, Mahdi
More informationEvolutionary Computation
Chapter 3 Evolutionary Computation Inspired by the success of nature in evolving such complex creatures as human beings, researchers in artificial intelligence have developed algorithms which are based
More informationA Fast Computational Genetic Algorithm for Economic Load Dispatch
A Fast Computational Genetic Algorithm for Economic Load Dispatch M.Sailaja Kumari 1, M.Sydulu 2 Email: 1 Sailaja_matam@Yahoo.com 1, 2 Department of Electrical Engineering National Institute of Technology,
More informationA Fast and Elitist Multiobjective Genetic Algorithm: NSGAII
182 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 6, NO. 2, APRIL 2002 A Fast and Elitist Multiobjective Genetic Algorithm: NSGAII Kalyanmoy Deb, Associate Member, IEEE, Amrit Pratap, Sameer Agarwal,
More informationLearning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning.
Learning Learning is essential for unknown environments, i.e., when designer lacks omniscience Artificial Intelligence Learning Chapter 8 Learning is useful as a system construction method, i.e., expose
More informationAPPLICATION OF ADVANCED SEARCH METHODS FOR AUTOMOTIVE DATABUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION
APPLICATION OF ADVANCED SEARCH METHODS FOR AUTOMOTIVE DATABUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION Harald Günther 1, Stephan Frei 1, Thomas Wenzel, Wolfgang Mickisch 1 Technische Universität Dortmund,
More informationAUTOMATIC ADJUSTMENT FOR LASER SYSTEMS USING A STOCHASTIC BINARY SEARCH ALGORITHM TO COPE WITH NOISY SENSING DATA
INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 1, NO. 2, JUNE 2008 AUTOMATIC ADJUSTMENT FOR LASER SYSTEMS USING A STOCHASTIC BINARY SEARCH ALGORITHM TO COPE WITH NOISY SENSING DATA
More informationRemoving the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm Shumeet Baluja & Rich Caruana May 22, 1995 CMUCS95141 School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 15213 This
More informationECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM
ECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM RAHUL GARG, 2 A.K.SHARMA READER, DEPARTMENT OF ELECTRICAL ENGINEERING, SBCET, JAIPUR (RAJ.) 2 ASSOCIATE PROF, DEPARTMENT OF ELECTRICAL ENGINEERING,
More informationAuthor's personal copy
Applied Soft Computing 11 (2011) 5652 5661 Contents lists available at ScienceDirect Applied Soft Computing j ourna l ho me p age: www.elsevier.com/l ocate/asoc A dynamic system model of biogeographybased
More information