# Genetic Algorithms in Test Pattern Generation

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1 Genetic Algorithms in Test Pattern Generation A Master Thesis Submitted to the Computer Engineering Department In fulfilment of the requirements for the Degree of Master of Science of Computer engineering by Eero Ivask Tallinn Technical University Tallinn 1998

2 2 Table of contents SUMMARY... 6 ANNOTATSIOON INTRODUCTION BASICS OF GENETIC ALGORITHMS REPRESENTATION INITIALIZATION FITNESS FUNCTION Fitness scaling Fitness function and noise REPRODUCTION Selection Crossover Mutation PARAMETERS FOR GENETIC ALGORITHM Population size Mutation rate Crossover rate Number of genetic generations WHY GENETIC ALGORITHMS WORK EXPLORATION ENGINE: IMPORTANT SIMILARITIES SCHEMA CONCEPT SCHEMA THEOREM IMPLICIT PARALLELISM BUILDING BLOCK HYPOTHESIS Practical aspects CONCLUSION GENETIC ALGORITHM FOR COMBINATIONAL CIRCUIT TESTING INTRODUCTION BASICS OF TEST DEVELOPMENT GENETIC FRAMEWORK Representation Initialization Evaluation of test vectors Fitness scaling Selection of candidate vectors Crossover Mutation in test vectors DESCRIPTION OF TEST GENERATION ALGORITHM Getting uniform random numbers EXPERIMENTAL RESULTS CONCLUSION GENETIC ALGORITHM FOR FINITE STATE MACHINE TESTING INTRODUCTION BASICS OF FINITE STATE MACHINE... 37

3 Definition Decision Diagrams for FSM Fault classes Limitations Test sequence Problems in FSM testing GENETIC FRAMEWORK Representation Initialization Evaluating test sequences Selection Crossover Mutation Description of program EXPERIMENTAL RESULTS FUTURE WORK Dynamically increasing test sequence length Improving fitness function Self-adaptive mutation rate Fault sampling Improved crossover CONCLUSION DISCUSSION REFERENCES APPENDIX A GETTING RANDOM NUMBERS APPENDIX B B.1 RESULT FILE (COMBINATIONAL CIRCUIT TESTING) B.2 TRACE FILE... 56

4 4 List of Figures Figure 1 Pseudo c- code for proportional roulette wheel selection Figure 2 Roulette wheel. Slots are proportional to individual s fitness Figure 3 Slot ranges corresponding to certain individuals Figure 4 Most common types of crossover Figure 5 Mutation in binary string Figure 6 Stack-at- faults detected for AND gate Figure 7 Stack-at-faults detected for OR gate Figure 8 One - point crossover Figure 9 C code implementation of one-point crossover Figure 10 Genetic test generation for combinational circuits Figure 11 Fault detection in time. Circuit c Figure 12 Fault detection in time. Circuit c Figure 13 STD and DD for benchmark circuit dk Figure 14 Memory structure of population Figure 15 Crossover of vector sequences Figure 16 One-point crossover producing two children

5 5 List of Tables Table 2-1 Hypothetical population and its fitness Table 2-2 Randomly selected numbers and corresponding individuals Table 3-1 Black box output maximization, inputs and fitness Table 4-1 Results for genetic test pattern generator Table 4-2 Results for random test pattern generator Table 4-3 Results for genetic test pattern generator and comparison with [18] Table 5-1 Characteristics of sample circuits Table 5-2 Experimental results for genetic FSM test generation... 46

6 6 Summary In current thesis, two test pattern generation approaches based on genetic algorithms are presented. The, first algorithm is designed so that it allows direct comparison with random method. Comparative results show that genetic algorithm performs better on large circuits, in the last stage of test generation when much of search effort must be made in order to detect still undetected faults. Experimental results on ISCAS'85 benchmarks [6] also show that the proposed algorithm performs significantly better than similar genetic approach published in [18]. In addition, the test sets generated by the algorithm here are more compact. The second algorithm presented in current thesis was aimed to solve the difficult problem of sequential circuit testing. Finite state machine model representation of the sequential circuit was targeted. Implemented prototype program can work standalone or together with the test generator introduced in [12] [13]. In latter case, evolutionary program tries to detect faults, which had remained undetected. Preliminary experimental results were given and further improvements to program were discussed.

8 8 1 Introduction Throughout the 1980s and the 1990, the theory and practise of testing electronic devices have changed considerably. As a consequence of exploiting the ever more advanced technologies, the complexities of devices today have increased significantly and so have the testing problems. This is because the ratio of I/O pins to the number of internal nodes decreases with the increasing level of integration and it leads to a decreased controllability and observability of internal nodes. In other words, observing the behaviour of internal node is more complicated if internal structure of network is more complex. These trends give increasing importance to automatic test pattern generation. Testing has become fundamental to the design, manufacture and delivery of quality product. In addition, several independent studies has shown that the cost of detecting a fault in a circuit is by magnitudes of order cheaper than to detect the same fault inside the system containing same circuit as a subsystem. The general problem of diagnosis is extremely difficult. Optimizing a diagnostic sequence of tests is known to be an NP-complete problem and deriving heuristics to improve practical diagnosis is a long-standing problem [1]. Artificial intelligence methods are therefore gained much of attention. One among these techniques is evolutionary algorithms or often referred as genetic algorithms. In common, genetic algorithms for test pattern generation use fault simulation for evaluating set of different possible test solutions. Better test solution, which detects more faults for example, receives higher score. Then new test set is built from pieces of previous solutions by doing certain bit-string manipulations and is evaluated by fault simulator again. In current thesis, the first aim was to compare genetic test generator with random generator, because it is known that in essence, genetic algorithm uses much of random numbers. Therefore, the algorithm is designed so that it allows direct comparison with random method. Experimental results on ISCAS'85 benchmarks [6] are given and comparison with a genetic test generation approach published in [18] is done. The experience and knowledge gained by implementing and experimenting on prototype program discussed above, was applied in design process of test generator for finite state machine. It was aimed to solve the difficult problem of sequential circuit testing. Implemented prototype program can work standalone or together with the test generator introduced in [12] [13]. In latter case, evolutionary program tries to detect faults, which had remained undetected. The thesis is organized as follows. Section 2 gives an overview of basic concept of genetic algorithms. Section 3 discusses why genetic algorithms have good performance. In section 4, genetic algorithm suitable for combinational circuit testing is developed, prototype program is implemented and comparative results with random test generation are presented. In addition, improvements to algorithm are suggested. In section 5, genetic algorithm suitable for finite state machine testing is developed, implemented and some preliminary results achieved on prototype program are presented. In addition, improvements to program are suggested. Appendix A discusses how to get unpredictable random numbers in computer implementation, as it is essential for genetic algorithms. Appendix B contains trace fail of program run.

9 9 2 Basics of genetic algorithms John Holland, the founder of the field of genetic algorithms points out in [3] the ability of simple representations (bit strings) to encode complicated structures and the power of simple transformations to improve such structures. Holland showed that with the proper control structure, rapid improvements of bit strings could occur (under certain transformations). Population of bit strings "evolves" as populations of animals do. An important formal result stressed by Holland was that even in large and complicated search spaces, given certain conditions on the problem domain, genetic algorithms would tend to converge on solutions that were globally optimal or nearly so. In order to solve a problem genetic algorithm must have following components: 1) A chromosomal representation of solution to the problem, 2) A way to create an initial population of solutions, 3) An evaluation function that plays the role of the environment, quality rating for solutions in terms of their "fitness" 4) Genetic operators that alter the structure of "children" during reproduction 5) Values for the parameters that genetic algorithm uses (population size, probabilities of applying genetic operators) 2.1 Representation In genetic framework, one possible solution to the problem is called individual. As we have different persons in society, we also have different solutions to the problem (one is more optimal than other is). All individuals together form population (society). We use binary representation for individuals (bit strings of 1's and 0's). Bit strings have been shown to be capable of usefully code variety of information, and they have been shown to be effective representations in unexpected domains. The properties of bit string representations for genetic algorithms have been extensively studied, and a good deal is known about genetic operators and parameter values that work well with them [3]. 2.2 Initialization For research purpose, random initializing a population is suitable. Moving from a randomly created population to a well adapted population is a good test of algorithm. Critical features of final solution will have been produced by the search and recombination mechanisms of the algorithm rather than the initialization procedures. To maximize the speed and quality of the final solution it is usually good to use direct methods for initializations. For example, output of another algorithm can be used or human solution to the problem.

10 Fitness function It is used to evaluate the fitness of the individuals in population (quality of the solutions). Better solutions will get higher score. Evaluation function directs population towards progress because good solutions (with high score) will be selected during selection process and pour solutions will be rejected Fitness scaling Many properties of evaluation function enhance or degrade a genetic algorithm's performance. One is normalization process used. Normalization is fitness scaling (increasing the difference between the fitness values). As a population converges on a definitive solution, the difference between fitness values may become very small. Best solutions can't have significant advantage in reproductive selection. For example, let us have scores 2 and 3. If these scores are used without any change as measures of each individual's fitness for reproduction, it will take some time before the descendants of good individual will gain the majority in the population. Fitness scaling solves this problem by adjusting the fitness values to the advantage of the most-fit solutions. For example, we can use squared values of fitness scores. Then, continuing example above we receive new scores 4 and 9 which are much more different now. The performance of a genetic algorithm is highly sensitive to normalization technique used. If it stresses improvements to much it will lead to driving out of alternative genetic material in the population, and will promote the rapid dominance of a single strain. When this happens, crossover becomes of little value, and the algorithm ands up intensively searching the solution space in the region of the last good individual found. If the normalization process does not stress good performance, the algorithm may fail to converge on good results in a reasonable time and will more likely to lose the best members of its population Fitness function and noise In some cases there will be no known fitness function that can accurately assess an individual s fitness, so an approximate (noisy) fitness function must be used. The noise inherent from noisy fitness function causes the selection process for reproduction to also be noisy [10]. We assume that a noisy fitness function returns a fitness score for an individual equal to the sum of real fitness of the individual plus some noise. Noisy information may come from a variety of sources, including noisy data, knowledge uncertainty, etc. To improve run time performance some genetic algorithms use fast but noisier fitness function instead of more accurate, but slower, fitness functoin that may also be available. Sampling fitness functions are a good example of this phenomena: fitness function uses smaller (reduced) sampler size to increase run time speed, at the expense of decreased accuracy of the fitness evaluation. 2.4 Reproduction During reproductive phase of genetic algorithm, individuals are selected from population and recombined, producing child (ren) which will be added into next generation. Parents are selected

11 11 randomly from the population using a scheme, which favours the more fit individuals. Good individuals will probably be selected several times in a generation, poor ones may not be at all. Having selected two parents, their genes are combined, typically using the mechanisms of crossover and mutation. Next, we take a closer look on genetic operators Selection Selection mechanism finds two (or more) candidates for crossover. The selection pressure is the degree to which the better individuals are favoured: the higher the selection pressure, the more the better individuals are favoured. This selection pressure drives the genetic algorithm to improve the population fitness over succeeding generations. The convergence rate of genetic algorithm is largely determined by the selection pressure. Higher selection pressures result higher convergence rates. Genetic algorithms are able to identify optimal or near optimal solutions under a wide range of selection pressure [9]. However, if the selection rate is too low, the convergence rate will be slow, and the genetic algorithm will unnecessarily take longer to find the optimal solution. If the selection pressure is too high, there is an increased change of genetic algorithm prematurely converging to an incorrect (sub-optimal) solution. There are several selection strategies possible: 1) Roulette wheel selection It is a standard gambler s roulette wheel, a spinning circle divided into several equal size sections. The croupier sets the wheel spinning and throws marble into bowl. After the motion of the wheel decreases, marble comes to rest in one of the numbered sections. In the case of genetic algorithm roulette wheel could be used to select individuals for further reproduction. The wheel corresponds to fitness array and the marble is a random unsigned integer less than the sum of all fitnesses in population. Let us look at Figure 1. There is c- coded fragment of roulette wheel interpretation for genetic algorithm. To find an individual int select_individual() { int j=0; long int juharv; juharv = floor (Random_btw_0_1() * fit_summa); } while (juharv > fit_mass[j]) { juharv -= fit_mass[j]; j++; } // returns index of the individual in population return (j); Figure 1 Pseudo c- code for proportional roulette wheel selection associated with the marbles landing place, the algorithm iterates through the fitness array. If the marbles value is less than the current fitness element, the corresponding individual

12 12 becomes a parent. Otherwise, the algorithm subtracts the current fitness value from the marble and then repeats the process with the next element in the fitness array. Thus, the largest fitness values tend to be the most likely resting places for the marble, since they use a larger area of the abstract wheel. That s why strategy described above is called proportional selection scheme. To clarify, let us look small example with a population size five. Table 2-1 shows the population and its corresponding fitness values. Individual Fitness Table 2-1 Hypothetical population and its fitness Total fitness of this population is 60. Figure 2 shows pie chart representing relative sizes of pie slices as assigned by fitness. We can also imagine that pie chart as our roulette wheel discussed above. What we actually see is a wheel with slots of different size. Sizes of slices correspond to roulette wheel slot sizes. We see, that individual has 34% chance of being selected as parent, whereas has only an 8% chance to be selected. Selecting five parents for example requires simply generating five random numbers, as shown in Table 2-2. Given a random number we determine a corresponding individual by looking at Figure 2 Figure 2 Roulette wheel. Slots are proportional to individual s fitness

13 13 Random num. Individual Table 2-2 Randomly selected numbers and corresponding individuals Figure 3 Slot ranges corresponding to certain individuals We see that individual (with the highest fitness) is two times a parent for a members of the new population, this is naturally allowable. Even the chromosome with the lowest fitness will be a parent once. However, second-most-fit chromosome did not reproduce- c est la vie, life is unpredictable. 2) Stochastic universal selection It is a less noisy version of roulette wheel selection in which N markers are placed around the roulette wheel, where N is a number of individuals in the population. N individuals are selected in a single spin of the roulette wheel, and the number of copies of each individual selected is equal to the number of markers inside the corresponding slot. 3) Tournament selection s individuals are taken at random, and the better individual is selected from them. It is possible to adjust its selection pressure by changing tournament size. The winner of the tournament is the individual with the highest fitness of the s tournament competitors, and the winner is inserted into mating pool. The mating pool, being filled with tournament winners, has a higher average fitness than the average population fitness. This fitness difference provides the selection pressure, which drives the genetic algorithm to improve the fitness of each succeeding generation. Tournament selection pressure can be increased (decreased) by increasing (decreasing) the

14 14 tournament size s, as the winner from larger tournament will, on average, have a higher fitness than the winner of a smaller tournament Crossover Exchanges corresponding genetic material from two parent, allowing useful genes on different parents to be combined in their offspring. Two parents may or may not be replaced in the original population for the next generation, these are different strategies. Crossover is the key to genetic algorithm's power. Most successful parents reproduce more often. Beneficial properties of two parents combine. Figure 4 shows most common crossover types. White and grey represent different individuals, their genetic material (genes). We can see how genes get mixed in every particular case of crossover. 1) One-point crossover 1 m m+1 L 1 m m+1 L 1 m m+1 L 1 m m+1 L 2) Two-point crossover 1 m m+1 r r+1 L 1 m m+1 r r+1 L 1 m m+1 r r+1 L 1 m m+1 r r+1 L 2) Uniform crossover 1 L 1 L 1 L 1 L Figure 4 Most common types of crossover

15 Mutation Random mutation provides background variation and occasionally introduces beneficial material into a species' chromosomes. Without the mutation, all the individuals in population will eventually be the same (because of exchange of genetic material) and there will be no progress anymore. We will be stuck in local maximum as it is used to say. Mutation in case of binary string is just inverting a bit as shown in Figure Figure 5 Mutation in binary string 2.5 Parameters for genetic algorithm In order to converge, several parameters of genetic algorithm have to be fine-tuned Population size Of course, for algorithm s convergence, bigger population is better, because we have more genetic material for selection and reproduction. Changes to build better individuals are bigger. However, there is one important aspect to consider though- aspect of algorithm s speed. Bigger population means more evaluation. Every individual in population has to be measured in terms of fitness. Usually this is computationally most expensive procedure in genetic algorithm. Therefore, population size is kept about 32. Of course it depends on evaluation complexity Mutation rate It has to be small enough not to loose useful individuals developed so far and big enough to provide population with new genetic material at the same time. Different sources of literature suggest values between 0.1 and 0.01.There exists interesting relation between population size and mutation rate. When we lower population size, then it is useful to increase the mutation rate and vice versa Crossover rate Normally, when two candidates are selected, they always do crossover.

16 Number of genetic generations Other words, how many cycles we do before we terminate. It depends on task s complexity. Sometimes hundred generations is enough, another time thousands of generations isn t enough. It is probably wise to stop when no improvement in solution quality is made for certain time. Limit condition can be calculated taking account task s dimensions. For complex task, we may allow more generations. When it is hard to determine appropriate a value for some parameter, it is good idea to use selfadaptation for that parameter. For example, we can change dynamically mutation rate along the progress of genetic algorithm: allowing mutations more often at the beginning and fewer mutations at the end, when solution needs only fine-tuning. Could be also that we need to use bigger mutation rate when progress has stopped, but we still are too far from plausible solution. So intelligent selfadaptation would be very promising.

17 17 3 Why genetic algorithms work In this section, questions like What is though manipulated by genetic algorithm, and how to know that this manipulation (what ever it could be) leads to optimum or near optimum results for particular problem? are answered. 3.1 Exploration engine: important similarities Fundamental question has been not answered for long time. If in optimisation process actually nothing else is used than values of fitness function, then what information could be extracted from a population of bit-strings and their fitness values? To clarify, let us consider bit-strings and fitness values in Table 3-1. This corresponds to black box output maximisation problem in [5]. Function to optimize is f (x) = x 2. String Fitness Table 3-1 Black box output maximization, inputs and fitness. So, what information could be extracted from this population to guide exploration toward progress? Actually, there is not much: four independent chains of bits and their fitness values. When examining closer, we naturally start evaluate column of bit-strings and we notice certain similarities between strings. Studying these similarities in detail, we can see that certain bits seem to be very correlated with good fitness results. It seems that all strings starting with 1 are within best ones for example. Could that be important element for optimisation of the function? Answer is yes. As we know, maximal value of the function f(x) = x 2 is obtained when argument x is maximal. In our 5 bit case x can be 31 in decimal coding which corresponds to in binary coding. In conclusion, we must look for similarities between strings in a population. Then, we have to seek for cause effect relation between similarities and improved performance. That way we have received a new source of information, which helps us guide exploration.

18 Schema concept Since important similarities can help to guide exploration, we are interested how one string can be associated with another class of strings containing invariant in a certain positions. A framework defined by schema concept will answer more or less these questions. Schema is a motive of similarity describing sub-set of bits in strings by similarities in certain positions, Holland [3]. To simplify we use just binary alphabet {0,1}, at the same time not loosing generality of discussion. We can easily introduce schemas by adding a special symbol * to this alphabet, which stands for indifference. Now we can create strings based on this ternary alphabet {0,1, *}. Importance of schema comes clear when it is used as tool for (bit) pattern identification: A schema identifies a string if there is correspondence in all positions in schema and in string under identification. Namely, 1 matches 1 and 0 matches 0 and * can match both 1 and 0. Let us take an example. We consider strings and schemas with length 5. The schema *0000 corresponds to two strings {10000, 00000}. Schema *111* describes set of four elements {01110, 01111, 11110, 11111}. As we see, idea of schema lets us easily consider all the similarities between the strings of finite length on finite alphabet. Note that * is just a meta-symbol, it is never manipulated by genetic algorithm, it is just notation which allows describe all the potential similarities between strings of particular length and on particular alphabet. Total number of similarities for previous example is 3 5, because each of five positions can take values 0, 1, or *. Generally, for the alphabet with cardinality of k (number of characters in alphabet) there exist (k+1) l schemas, where l is length of schema. So, what quantity of useful information we have received taking account similarities? The answer lies in number of unique schemas contained in population. In order to count these, we need to know strings in the population. What we can do is to count schemas related to one unique string; such a way we obtain number of upper bound of total schemas in a population. For example, we consider a string with length of schemas belong to that string, because each position can take its actual value or indifference symbol *. Generally expressed, a string contains 2 l schemas. Therefore, a population with size n contains between 2 l and n*2 l schemas, depending on its diversity. As we see, it is much of information to help in guiding of exploration. Taking account similarities is justified. It is also obvious that efficient (parallel) treatment is needed if we want to use all that information in reasonable time. Well, we know now the number of schemas, but how many of them are effectively treated by genetic algorithm? In order to answer that, we have to consider the effects of genetic operators (selection, crossover, and mutation) on development or disappearing of important schemas from generation to generation. Effect of selection on schema is easy to determine. Strings better adopted are selected with greater probability. Generally, schemas resembling the best ones are selected without loosing their advantage. Meanwhile, selection only is not capable of exploring new points in the space of search. Maybe crossover enters the game now. The crossover leaves schema intact when it is not cutting it, but in the opposite case it can degrade it. For example, let us consider two schemas 1***0 and **11*. First schema has great changes to be destroyed by crossover (useful bits 1 and 0 will be probably separated). Second schema will probably be preserved (bits are close to each other). Consequently, schemas with short (useful) length are conserved by crossover, and are multiplied in quantity by selection operator. Mutation with normal (low) rate rarely destroys schemas

19 19 as observations show. Schemas which are well adopted and with short useful length, (elementary blocks, building blocks) are promoted from generation to generation. It is due to exponential growth of number of trials what are given to the best representatives. All this will happen in parallel, not requiring days of computing and much more memory than necessary for describing the population of n strings. 3.3 Schema theorem So far, we have seen that there is great number of similarities to exploit in a population of strings. Intuitively we have seen how genetic algorithm exploits all the similarities contained in elementary blocks (schemas with good performance). Now we are going to look at these observations in a more formal way. We will find precise number of schemas in a population. We will formally study, which schemas will multiply in quantity and which, will decease in a generation. We will conclude with fundamental theorem of genetic algorithm. To analyse development and degrading of great number of schemas contained in a population formal notations are needed. We are going to consider the effects of selection, crossover and mutation on schemas in a population. We assume (without loosing generality) that we have binary strings on alphabet V = {0,1,*}. Where * stands for indifference (0 or 1) as we saw in 3.2. Let us define strings by capital letters and positions in string with small letters. For example, string of seven bits A = can be referred as: A = a 1 a 2 a 3 a 4 a 5 a 6 a 7 Bit positions can be reordered, for example: A = a 3 a 2 a 1 a 4 a 5 a 7 a 6 Set of strings A j, j = 1,2 n, form a population A (t) in a time moment (or in generation) t. Let us recall that there was 3 l schemas possible (similarities) based on binary string length l. Generally expressing, there was (k+1) l schemas for alphabet of cardinality of k. In addition, there was n*2 l schemas in a population with size n, because each string itself contains 2 l schemas. This quantity schows importance of information treated by genetic algorithms. At the same time, in order to isolate elementary building blocks (for further investigation) in that vast quantity of information, we have to be able to make distinction among different type of schemas. Not all the schemas are created to be equal. Some of them are more specific than others are. For example, schema 011*1** tells more about of it s important similarities than schema 0******. Even more, some schemas cover bigger part of the whole length of the string than others. For example, 1****1* covers bigger part of string than 1*1****. In order to quantify these notations, we are going to define two characteristics of schema: it s rank and useful length.

20 20 Rank of the schema H, o(h) is just number of the positions instantiated (0 or 1 in case of binary alphabet) in a sample. For example, for a 011*1** rank is 4 (o (011*1**) = 4), but for 0****** rank is 1. Useful length of schema H, δ(h) is the distance between first and last instantiated positions in the string. For example, for schema 011*1** useful length is 4, because last position instanciated is 5 and first position is 1, and the distance what separates them is δ(h) = 5-1 = 4. For another schema 0******, it is particularly simply to calculate. Because there is only one position instanciated, useful length will be 0. The schemas and properties defined so far, are instruments, which allow formally study and classify similarities between strings. Moreover, it is possible to analyse effects of genetic operators on elementary building blocks contained in population. What we are going to do now, is to consider the effects of selection, crossover and mutation (first separately and then combined) on schemas contained in population. Effect of the selection on the expected number of schemas in a population is relatively easy to determine. Let us suppose that in given time moment t there is m exemplars of schema H in the population. We describe that m = m (H,t). Number of schemas can vary in different time moments. In case of selection, a string is copied into next generation according to its fitness function (degree of adoption). More precisely, a string A i is selected with probability p i f i = f i Having population with size n, we expect to have ( ) mht (, + 1 ) = mht (, ) n f H (1) f i representatives of schema in a population in a time moment t + 1, where f(h) is average fitness of the string represented by schema H in a time moment t. As average fitness of the population can be expressed as following: f f i =, n then we can rewrite expression (1) as following: f ( H) 1 (2) f (, + ) = mht (, ) mht Clearly saying, any schema develops (reproduces) with the speed which is equal (proportionate) to the ratio of schema s average fitness and population s average fitness. In other words, schema, which

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