Numerical Research on Distributed Genetic Algorithm with Redundant

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Numerical Research on Distributed Genetic Algorithm with Redundant"

Transcription

1 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 Abstract Genetic algorithm (GA) is one of optimization algorithm based on an idea for evolution of life. GA can be applied various combination optimization problem. This paper proposes a novel distributed genetic algorithm (DGA) with redundant binary number. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease. The effectiveness of the proposed algorithm was confirmed by computer simulations. 1. Introduction Keywords: Genetic Algorithm, Distributed Ga, Redundant Binary Number Genetic algorithm (GA) is one of optimization algorithm based on an idea for evolution of life [1,2]. GA can be applied various problems such as combination optimization problem, machine learning and so on. A distributed genetic algorithm (DGA) divides a solution group into some solution groups island, and performs genetic operation in each island [3]. In DGA, in order to exchange the solution among each island, migration operation is performed. Since DGA has few numbers of individuals per island, premature convergence takes place easily. However, since diversity is also maintainable by migration, compared with conventional GA, effective solution search is expectable. This paper proposes a novel DGA with redundant binary number, while conventional DGA expresses chromosomes in binary number [4,5]. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease [6]. In the proposed algorithm, different numerical systems are used on each island. Therefore, since diversity is further maintainable, the further improvement in performance is expectable. The effectiveness of the proposed algorithm was confirmed by computer simulations. 2. Genetic Algorithm Genetic algorithm (GA) is proposed in 1975 by Prof. John Holland. The algorithm is based on Darwin's evolutionary theory and likens solution to gene. The flow chart of GA is shown in figure 1. Figure 1. Flow Chart of GA International Journal of Intelligent Information Processing (IJIIP) Volume3, Number4, Dec 2012 doi: /ijiip.vol3.issue4.3 21

2 The procedure of GA is as follows. (1) Initialization: The first process decides initial genotype, namely value and genetic length. For example, if we assume values are 0 or 1, and length is eight, an example of chromosome is shown in figure 2. (2) Evaluation: The second process calculates the fitness for each individual with the target function. The evaluation depends on each problem. (3) Termination Judgment: If the process satisfies the termination condition, the operation finishes and outputs the individual with the best fitness as the optimized solution. (4) Selection: To generate the children, this process chooses parents from individuals. For example, if we assume parents the first generation, children become the second generation. The children generate the next children again. The children inherited the characteristic of parents are generated in this way. (5) Crossover: This process crosses individuals chosen by selection operation and generates the individuals of the next generation. An example of crossover operation is shown in figure 3. (6) Mutation: This process mutates the chromosome of new generation (figure 4). The mutation operation is effective to escape from a local optimum solution. Figure 2. Chromosome Figure 3. Crossover Operation Figure 4. Mutation Operation A. Distributed Genetic Algorithm A distributed genetic algorithm (DGA) divides a solution group into some solution groups island, and performs genetic operation in each island. In DGA, in order to exchange the solution among each island, migration operation is performed (figure 5). Since DGA has few numbers of individuals per island, premature convergence takes place easily. However, since diversity is also maintainable by migration, compared with conventional GA, effective solution search is expectable. Figure 6 shows a simple flow chart of DGA. Figure 5. Migration Operation 22

3 B. Redundant Binary Number Figure 6. Flow Chart of DGA In the proposed algorithm, redundant binary number system is utilized. The advantage of GA with redundant binary number is increase of total expression number of optimized solution. From this advantage, improvement in searching speed and decrease of error rate are expected. The redundant binary number uses values 0, 1 and -1. However, because conventional digital circuits cannot deal the value -1, we express each genetic information in two bits. We assume that 0 is assigned to 00 or 11, 1 is assigned to 01, -1 is assigned to 10. These correspondences are summarized in table 1. Table 1. Expression of Redundant Binary Number Bit String , For example, we express decimal number seven in binary number of the four bits precision and the corresponding redundant binary numbers, as shown in figure 7. The chromosomes of the redundant binary number become longer in comparison with the normal binary number. However, there are many expression way. For example, there are nine expression ways in the case of figure 7. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease. Figure 7. Comparison between Binary Number and Redundant Binary Number 23

4 The GA based on redundant binary number is almost the same as conventional GA. However, decoding from redundant binary number to binary number is required. In the proposed algorithm, we separate chromosomes into odd number bit and even number bit. Then we subtract even number bit from odd number bit. An example is shown in figure Proposed DGA Figure 8. Decoding from Redundant Binary Number to Binary Number The concept of DGA proposed in this paper is shown in figure 9. In this figure, GA_B, GA_G, and GA_RB express GA using binary number, GA using Gray code, and GA using redundant binary number, respectively. A part of solutions are exchanged through the migration unit. In the migration, code conversions are performed. Figure 10 shows the migration pattern. The random number r chooses the migration pattern. The number of the chromosomes which emigrate is one. Figure 9. Concept of proposed DGA Figure 10. Migration Pattern 24

5 Although search results depend on type of solution code, stable good results are expected to many problems by the proposed DGA. In figure 9, although there are two sets of GA with binary number, one set of GA with Gray code, and one set of GA with redundant binary number, this is just an example. Of course, various combinations are possible. The authors are planning to develop the processor for this algorithm. Then, in order to suppress the circuit scale, the composition of figure 9 was illustrated. Namely, since the scale is small, two sets of circuits treating a binary number are used, and since the scale is large, as for the circuit treating a gray code or a redundant binary number, only one set is used. 4. Simulation The comparison of simulation results were performed in four types of GA (binary number, Gray code, redundant binary and proposed DGA). The performance of each GA was evaluated by solving following three functions. The corresponding graphs are illustrated in figure 11, 12 and 13, respectively. (Eq. 1) f ( x) x (Solution: x 141) Figure 11. Graph of Equation 1 (Eq. 2) f ( x) x (Solution: x 44721) Figure 12. Graph of Equation 2 25

6 (Eq. 3) 2 f ( x) ( x 100)( x 40000) (Solution: x ) Figure 13. Graph of Equation 3 The simulation program was implemented in C language and executed on HP Z400 workstation. The error rates of three functions are summarized in figure 14. Each result is the average of 500 times of trial. In calculation of error rate, only the case where a solution is completely same as the optimal solution is judged as a correct solution. Therefore, if a solution is not in agreement with the optimal solution, even if very close to the optimal solution, it has judged as an error. Figure 14 shows that good results were obtained by the proposed DGA in various problems. The parameters are summarized in table 2. Although the solution is denoted by 16 bits in GA with binary number and Gray code, the solution is denoted by 32 bits in GA with redundant binary number. Figure 14. Error rate Table 2. GA Parameters Generation 100 Population 32 (8 x 4) Selection Ranking Crossover One point Crossover rate 1 Mutation rate

7 5. Conclusion In this paper, a novel distribute genetic algorithm with redundant binary number was proposed and discussed. The proposed algorithm showed stable and good performance by combining the various coding system. It is confirmed that the proposed algorithm was effective for improvement of error rate by computer simulation results. The future works are evaluation for other difficult problems and hardware implementation. 6. Acknowledgment This work was supported by Tokyo Denki University Science Promotion Fund (Q12J-03). 7. References [1] L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, [2] X. Yao, Y. Liu, and G. M. Lin, Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, Vol. 3, No. 2, pp , 1999 [3] R. Tanese, Distributed Genetic Algorithms, Proceeding of the 3rd International Conference on Genetic Algorithms, pp , [4] A. Murayama, A Study of Genetic Algorithm based on Redundant Binary Number, Master's Thesis of Tokyo Denki University, [5] S. Seto, A. Kanasugi, A Novel Distributed Genetic Algorithm with Redundant Binary Number, Proceeding of AICIT International Conference on Information Science and Digital Content Technology, pp , [6] M. Aoshima, A. Kanasugi, A Processor for Genetic Algorithm based on Redundant Binary Number, Journal of AICIT Next Generation Information Technology, Vol. 1, No. 3, pp , [7] A. Murayama, A. Kanasugi, A novel coding method for genetic algorithms, International Journal of Artificial Life and Robotics, Vol. 15, No. 3, pp , [8] A. Kanasugi, A. Tsukahara, A Processor for Genetic Algorithm using Dynamically Reconfigurable Memory, Proceeding of International Conference on Hybrid Information Technology, pp , [9] P. Graham, B. Nelson, A hardware genetic algorithm for the traveling salesman problem on SPLASH2, Proceeding of International Workshop on Field Lecture Notes In Computer Science, Vol. 975, pp , [10] A. Tsukahara, A. Kanasugi, Genetic Algorithm with Dynamic Variable Number of Individuals and Accuracy, International Journal of Control, Automation, and Systems, Vol. 7, No.1, pp. 1-6,

A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number

A 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 information

Evolutionary SAT Solver (ESS)

Evolutionary 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 3-5, 2011,

More information

ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013

ISSN: 2319-5967 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 information

A Robust Method for Solving Transcendental Equations

A Robust Method for Solving Transcendental Equations www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,

More information

Comparison of Major Domination Schemes for Diploid Binary Genetic Algorithms in Dynamic Environments

Comparison 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 information

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0 212 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 information

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

Introduction To Genetic Algorithms

Introduction 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 information

Genetic Algorithm Based Interconnection Network Topology Optimization Analysis

Genetic Algorithm Based Interconnection Network Topology Optimization Analysis Genetic Algorithm Based Interconnection Network Topology Optimization Analysis 1 WANG Peng, 2 Wang XueFei, 3 Wu YaMing 1,3 College of Information Engineering, Suihua University, Suihua Heilongjiang, 152061

More information

International Journal of Software and Web Sciences (IJSWS) www.iasir.net

International Journal of Software and Web Sciences (IJSWS) www.iasir.net International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk

Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk BMAS 2005 VHDL-AMS based genetic optimization of a fuzzy logic controller for automotive active suspension systems Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk Outline Introduction and system

More information

Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm

Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College

More information

Genetic Algorithm Evolution of Cellular Automata Rules for Complex Binary Sequence Prediction

Genetic 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. 1-6 Genetic Algorithm Evolution of Cellular Automata Rules

More information

ON SUITABILITY OF FPGA BASED EVOLVABLE HARDWARE SYSTEMS TO INTEGRATE RECONFIGURABLE CIRCUITS WITH HOST PROCESSING UNIT

ON SUITABILITY OF FPGA BASED EVOLVABLE HARDWARE SYSTEMS TO INTEGRATE RECONFIGURABLE CIRCUITS WITH HOST PROCESSING UNIT 216 ON SUITABILITY OF FPGA BASED EVOLVABLE HARDWARE SYSTEMS TO INTEGRATE RECONFIGURABLE CIRCUITS WITH HOST PROCESSING UNIT *P.Nirmalkumar, **J.Raja Paul Perinbam, @S.Ravi and #B.Rajan *Research Scholar,

More information

Alpha Cut based Novel Selection for Genetic Algorithm

Alpha 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 information

Optimal PID Controller Design for AVR System

Optimal PID Controller Design for AVR System Tamkang Journal of Science and Engineering, Vol. 2, No. 3, pp. 259 270 (2009) 259 Optimal PID Controller Design for AVR System Ching-Chang Wong*, Shih-An Li and Hou-Yi Wang Department of Electrical Engineering,

More information

Programming Risk Assessment Models for Online Security Evaluation Systems

Programming 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 Babes-Bolyai

More information

Genetic algorithms for changing environments

Genetic 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 information

Management Science Letters

Management Science Letters Management Science Letters 4 (2014) 905 912 Contents lists available at GrowingScience Management Science Letters homepage: www.growingscience.com/msl Measuring customer loyalty using an extended RFM and

More information

GA as a Data Optimization Tool for Predictive Analytics

GA 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 information

A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation

A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation Abhishek Singh Department of Information Technology Amity School of Engineering and Technology Amity

More information

PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM

PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM Md. Shahjahan Kabir 1, Kh. Mohaimenul Kabir 2 and Dr. Rabiul Islam 3 1 Dept. of CSE, Dhaka International University, Dhaka, Bangladesh

More information

Architecture bits. (Chromosome) (Evolved chromosome) Downloading. Downloading PLD. GA operation Architecture bits

Architecture bits. (Chromosome) (Evolved chromosome) Downloading. Downloading PLD. GA operation Architecture bits A Pattern Recognition System Using Evolvable Hardware Masaya Iwata 1 Isamu Kajitani 2 Hitoshi Yamada 2 Hitoshi Iba 1 Tetsuya Higuchi 1 1 1-1-4,Umezono,Tsukuba,Ibaraki,305,Japan Electrotechnical Laboratory

More information

A Hybrid Tabu Search Method for Assembly Line Balancing

A Hybrid Tabu Search Method for Assembly Line Balancing Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 15-17, 2007 443 A Hybrid Tabu Search Method for Assembly Line Balancing SUPAPORN

More information

A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms

A 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 information

Solving Timetable Scheduling Problem by Using Genetic Algorithms

Solving 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 information

A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN)

A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN) ISSN: 2278 1323 All Rights Reserved 2014 IJARCET 3910 A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN) Miss: KIRTI JOSHI Abstract A Genetic Algorithm (GA) is an intelligent search

More information

AUTOMATIC ADJUSTMENT FOR LASER SYSTEMS USING A STOCHASTIC BINARY SEARCH ALGORITHM TO COPE WITH NOISY SENSING DATA

AUTOMATIC 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 information

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,

More information

New binary representation in Genetic Algorithms for solving TSP by mapping permutations to a list of ordered numbers

New binary representation in Genetic Algorithms for solving TSP by mapping permutations to a list of ordered numbers Proceedings of the 5th WSEAS Int Conf on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 0-, 006 363 New binary representation in Genetic Algorithms for solving

More information

ECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM

ECONOMIC 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 information

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province Research on a Heuristic GA-Based 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 information

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem American Journal of Industrial and Business Management, 2016, 6, 774-789 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ajibm http://dx.doi.org/10.4236/ajibm.2016.66071 A Study of Crossover

More information

A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems

A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems IEEE International Conference on Evolutionary Computation May 4-11 1998, Anchorage, Alaska A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems Sakae Toune, Hiroyuki Fudo,

More information

Holland s GA Schema Theorem

Holland 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 information

A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Available at http://www.joics.com A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

More information

SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD

SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD Jaya Srivastaval 1 and Twinkle Dwivedi 2 1 Department of Computer Science & Engineering, Shri Ramswaroop Memorial

More information

Practical Applications of Evolutionary Computation to Financial Engineering

Practical Applications of Evolutionary Computation to Financial Engineering Hitoshi Iba and Claus C. Aranha Practical Applications of Evolutionary Computation to Financial Engineering Robust Techniques for Forecasting, Trading and Hedging 4Q Springer Contents 1 Introduction to

More information

Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects

Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects Journal of Computer Science 2 (2): 118-123, 2006 ISSN 1549-3636 2006 Science Publications Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects Alaa F. Sheta Computers

More information

Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System

Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System Abstract: In recent years, many brokerage firms and hedge funds use a trading system based on financial

More information

CHAPTER 6 GENETIC ALGORITHM OPTIMIZED FUZZY CONTROLLED MOBILE ROBOT

CHAPTER 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 information

Towards Heuristic Web Services Composition Using Immune Algorithm

Towards Heuristic Web Services Composition Using Immune Algorithm Towards Heuristic Web Services Composition Using Immune Algorithm Jiuyun Xu School of Computer & Communication Engineering China University of Petroleum xujiuyun@ieee.org Stephan Reiff-Marganiec Department

More information

Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier

Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier D.Nithya a, *, V.Suganya b,1, R.Saranya Irudaya Mary c,1 Abstract - This paper presents,

More information

Vol. 35, No. 3, Sept 30,2000 ملخص تعتبر الخوارزمات الجينية واحدة من أفضل طرق البحث من ناحية األداء. فبالرغم من أن استخدام هذه الطريقة ال يعطي الحل

Vol. 35, No. 3, Sept 30,2000 ملخص تعتبر الخوارزمات الجينية واحدة من أفضل طرق البحث من ناحية األداء. فبالرغم من أن استخدام هذه الطريقة ال يعطي الحل AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING Vol. 35, No. 3, Sept 30,2000 SCIENTIFIC BULLETIN Received on : 3/9/2000 Accepted on: 28/9/2000 pp : 337-348 GENETIC ALGORITHMS AND ITS USE WITH BACK- PROPAGATION

More information

A Novel Binary Particle Swarm Optimization

A 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 information

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster , pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing

More information

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM MS. DIMPI K PATEL Department of Computer Science and Engineering, Hasmukh Goswami college of Engineering, Ahmedabad, Gujarat ABSTRACT The Internet

More information

College of information technology Department of software

College 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/2011-2012 ***************************************************************************

More information

Lab 4: 26 th March 2012. Exercise 1: Evolutionary algorithms

Lab 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 information

New 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 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 information

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational

More information

A Performance Comparison of GA and ACO Applied to TSP

A Performance Comparison of GA and ACO Applied to TSP A Performance Comparison of GA and ACO Applied to TSP Sabry Ahmed Haroun Laboratoire LISER, ENSEM, UH2C Casablanca, Morocco. Benhra Jamal Laboratoire LISER, ENSEM, UH2C Casablanca, Morocco. El Hassani

More information

Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem

Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem Vikas Thada Asst. Prof (CSE), ASET, Amity University, Gurgaon, India Shivali Dhaka Asst. Prof (CSE), ASET, Amity

More information

Stock price prediction using genetic algorithms and evolution strategies

Stock price prediction using genetic algorithms and evolution strategies Stock price prediction using genetic algorithms and evolution strategies Ganesh Bonde Institute of Artificial Intelligence University Of Georgia Athens,GA-30601 Email: ganesh84@uga.edu Rasheed Khaled Institute

More information

Genetic 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 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 information

Proceedings of the First IEEE Conference on Evolutionary Computation - IEEE World Congress on Computational Intelligence, June

Proceedings of the First IEEE Conference on Evolutionary Computation - IEEE World Congress on Computational Intelligence, June Proceedings of the First IEEE Conference on Evolutionary Computation - IEEE World Congress on Computational Intelligence, June 26-July 2, 1994, Orlando, Florida, pp. 829-833. Dynamic Scheduling of Computer

More information

Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling

Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling Proceedings of the th Hawaii International Conference on System Sciences - 00 Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling Yasuhiro Tsujimura Yuichiro Mafune Mitsuo Gen

More information

Original Article Efficient Genetic Algorithm on Linear Programming Problem for Fittest Chromosomes

Original Article Efficient Genetic Algorithm on Linear Programming Problem for Fittest Chromosomes International Archive of Applied Sciences and Technology Volume 3 [2] June 2012: 47-57 ISSN: 0976-4828 Society of Education, India Website: www.soeagra.com/iaast/iaast.htm Original Article Efficient Genetic

More information

Extended Finite-State Machine Inference with Parallel Ant Colony Based Algorithms

Extended Finite-State Machine Inference with Parallel Ant Colony Based Algorithms Extended Finite-State Machine Inference with Parallel Ant Colony Based Algorithms Daniil Chivilikhin PhD student ITMO University Vladimir Ulyantsev PhD student ITMO University Anatoly Shalyto Dr.Sci.,

More information

Demand Forecasting Optimization in Supply Chain

Demand Forecasting Optimization in Supply Chain 2011 International Conference on Information Management and Engineering (ICIME 2011) IPCSIT vol. 52 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V52.12 Demand Forecasting Optimization

More information

A Genetic Algorithm-Evolved 3D Point Cloud Descriptor

A Genetic Algorithm-Evolved 3D Point Cloud Descriptor A Genetic Algorithm-Evolved 3D Point Cloud Descriptor Dominik Wȩgrzyn and Luís A. Alexandre IT - Instituto de Telecomunicações Dept. of Computer Science, Univ. Beira Interior, 6200-001 Covilhã, Portugal

More information

Genetic 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 Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods

More information

Energy Efficient Load Balancing of Virtual Machines in Cloud Environments

Energy Efficient Load Balancing of Virtual Machines in Cloud Environments , pp.21-34 http://dx.doi.org/10.14257/ijcs.2015.2.1.03 Energy Efficient Load Balancing of Virtual Machines in Cloud Environments Abdulhussein Abdulmohson 1, Sudha Pelluri 2 and Ramachandram Sirandas 3

More information

Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies

Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com

More information

Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com

Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com Neural Network and Genetic Algorithm Based Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com Consider the challenge of constructing a financial market trading system using commonly available technical

More information

Software Engineering and Service Design: courses in ITMO University

Software Engineering and Service Design: courses in ITMO University Software Engineering and Service Design: courses in ITMO University Igor Buzhinsky igor.buzhinsky@gmail.com Computer Technologies Department Department of Computer Science and Information Systems December

More information

Genetic Algorithm for Solving Simple Mathematical Equality Problem

Genetic Algorithm for Solving Simple Mathematical Equality Problem Genetic Algorithm for Solving Simple Mathematical Equality Problem Denny Hermawanto Indonesian Institute of Sciences (LIPI), INDONESIA Mail: denny.hermawanto@gmail.com Abstract This paper explains genetic

More information

E-mail Spam Filtering Using Genetic Algorithm: A Deeper Analysis

E-mail Spam Filtering Using Genetic Algorithm: A Deeper Analysis ISSN:0975-9646 Mandeep hodhary et al, / (IJSIT) International Journal of omputer Science and Information Technologies, Vol. 6 (5), 205, 4266-4270 E-mail Spam Filtering Using Genetic Algorithm: A Deeper

More information

A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem

A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical

More information

Using Genetic Algorithm for Network Intrusion Detection

Using Genetic Algorithm for Network Intrusion Detection Using Genetic Algorithm for Network Intrusion Detection Wei Li Department of Computer Science and Engineering Mississippi State University, Mississippi State, MS 39762 Email: wli@cse.msstate.edu Abstract

More information

Intelligent Modeling of Sugar-cane Maturation

Intelligent Modeling of Sugar-cane Maturation Intelligent Modeling of Sugar-cane Maturation State University of Pernambuco Recife (Brazil) Fernando Buarque de Lima Neto, PhD Salomão Madeiro Flávio Rosendo da Silva Oliveira Frederico Bruno Alexandre

More information

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Mahyar Shahsavari, Zaid Al-Ars, Koen Bertels,1, Computer Engineering Group, Software & Computer Technology

More information

A Service Revenue-oriented Task Scheduling Model of Cloud Computing

A Service Revenue-oriented 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 Revenue-oriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,

More information

Inventory Analysis Using Genetic Algorithm In Supply Chain Management

Inventory Analysis Using Genetic Algorithm In Supply Chain Management Inventory Analysis Using Genetic Algorithm In Supply Chain Management Leena Thakur M.E. Information Technology Thakur College of Engg & Technology, Kandivali(E) Mumbai-101,India. Aaditya A. Desai M.E.

More information

Genetic Algorithms and Sudoku

Genetic 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 57701-3995 john.weiss@sdsmt.edu MICS 2009

More information

Performance of Hybrid Genetic Algorithms Incorporating Local Search

Performance 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 information

Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com

Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com A Dynamic Deployment Policy of Slave Controllers for Software Defined Network Yongqiang Yang and Gang Xu College of Computer

More information

Intelligent Agents Serving Based On The Society Information

Intelligent Agents Serving Based On The Society Information Intelligent Agents Serving Based On The Society Information Sanem SARIEL Istanbul Technical University, Computer Engineering Department, Istanbul, TURKEY sariel@cs.itu.edu.tr B. Tevfik AKGUN Yildiz Technical

More information

Optimization of PID parameters with an improved simplex PSO

Optimization of PID parameters with an improved simplex PSO Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s13660-015-0785-2 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Ji-min Li 1, Yeong-Cheng

More information

Acoustic Design of Theatres Applying Genetic Algorithms

Acoustic Design of Theatres Applying Genetic Algorithms Acoustic Design of Theatres Applying Genetic Algorithms Shin-ichi Sato a), Tatsuro Hayashi, Atsushi Takizawa, Akinori Tani, Hiroshi Kawamura, Yoichi Ando Graduate School of Science and Technology, Kobe

More information

KNOWLEDGE MANAGEMENT, ORGANIZATIONAL INTELLIGENCE AND LEARNING, AND COMPLEXITY - Vol. I - Genetic Algorithms - Calabretta R.

KNOWLEDGE MANAGEMENT, ORGANIZATIONAL INTELLIGENCE AND LEARNING, AND COMPLEXITY - Vol. I - Genetic Algorithms - Calabretta R. GENETIC ALGORITHMS Calabretta R. Institute of Cognitive Sciences and Technologies, National Research Council, Italy Keywords: Simulation, computational models of evolution, evolutionary computation, evolutionary

More information

USING GENETIC ALGORITHM IN NETWORK SECURITY

USING GENETIC ALGORITHM IN NETWORK SECURITY USING GENETIC ALGORITHM IN NETWORK SECURITY Ehab Talal Abdel-Ra'of Bader 1 & Hebah H. O. Nasereddin 2 1 Amman Arab University. 2 Middle East University, P.O. Box: 144378, Code 11814, Amman-Jordan Email:

More information

Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm

Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm IOSR Journal of Engineering (IOSRJEN) ISSN: 2250-3021 Volume 2, Issue 7(July 2012), PP 141-147 Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm 1 Sourav Banerjee, 2 Mainak Adhikari,

More information

Architectural Design for Space Layout by Genetic Algorithms

Architectural Design for Space Layout by Genetic Algorithms Architectural Design for Space Layout by Genetic Algorithms Özer Ciftcioglu, Sanja Durmisevic and I. Sevil Sariyildiz Delft University of Technology, Faculty of Architecture Building Technology, 2628 CR

More information

Genetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm

Genetic 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 information

A Multi-objective Genetic Algorithm for Employee Scheduling

A Multi-objective Genetic Algorithm for Employee Scheduling A Multi-objective Genetic Algorithm for Scheduling Russell Greenspan University of Illinois December, rgreensp@uiuc.edu ABSTRACT A Genetic Algorithm (GA) is applied to an employee scheduling optimization

More information

Improved Multiprocessor Task Scheduling Using Genetic Algorithms

Improved Multiprocessor Task Scheduling Using Genetic Algorithms From: Proceedings of the Twelfth International FLAIRS Conference. Copyright 999, AAAI (www.aaai.org). All rights reserved. Improved Multiprocessor Task Scheduling Using Genetic Algorithms Michael Bohler

More information

A Fast Computational Genetic Algorithm for Economic Load Dispatch

A 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 information

A Novel Cryptographic Key Generation Method Using Image Features

A Novel Cryptographic Key Generation Method Using Image Features Research Journal of Information Technology 4(2): 88-92, 2012 ISSN: 2041-3114 Maxwell Scientific Organization, 2012 Submitted: April 18, 2012 Accepted: May 23, 2012 Published: June 30, 2012 A Novel Cryptographic

More information

The Use of Evolutionary Algorithms in Data Mining. Khulood AlYahya Sultanah AlOtaibi

The Use of Evolutionary Algorithms in Data Mining. Khulood AlYahya Sultanah AlOtaibi The Use of Evolutionary Algorithms in Data Mining Ayush Joshi Jordan Wallwork Khulood AlYahya Sultanah AlOtaibi MScISE BScAICS MScISE MScACS 1 Abstract With the huge amount of data being generated in the

More information

AS part of the development process, software needs to

AS part of the development process, software needs to Dynamic White-Box Software Testing using a Recursive Hybrid Evolutionary Strategy/Genetic Algorithm Ashwin Panchapakesan, Graduate Student Member, Rami Abielmona, Senior Member, IEEE, and Emil Petriu,

More information

An Ant Colony Optimization Approach to the Software Release Planning Problem

An Ant Colony Optimization Approach to the Software Release Planning Problem SBSE for Early Lifecyle Software Engineering 23 rd February 2011 London, UK An Ant Colony Optimization Approach to the Software Release Planning Problem with Dependent Requirements Jerffeson Teixeira de

More information

D A T A M I N I N G C L A S S I F I C A T I O N

D A T A M I N I N G C L A S S I F I C A T I O N D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.

More information

The 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 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 information

Optimal Tuning of PID Controller Using Meta Heuristic Approach

Optimal Tuning of PID Controller Using Meta Heuristic Approach International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 2 (2014), pp. 171-176 International Research Publication House http://www.irphouse.com Optimal Tuning of

More information

The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation

The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation Li Lin, Longbing Cao, Jiaqi Wang, Chengqi Zhang Faculty of Information Technology, University of Technology, Sydney, NSW

More information

Influence 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 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 information

Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Proceedings of the World Congress on Engineering Vol II WCE, July 6-8,, London, U.K. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Noraini Mohd Razali, John Geraghty

More information

Evolutionary Prefetching and Caching in an Independent Storage Units Model

Evolutionary 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 E-mail: avakali@csdauthgr Abstract Modern applications

More information

A Genetic-Fuzzy Logic Based Load Balancing Algorithm in Heterogeneous Distributed Systems

A Genetic-Fuzzy Logic Based Load Balancing Algorithm in Heterogeneous Distributed Systems A Genetic-Fuzzy Logic Based Load Balancing Algorithm in Heterogeneous Distributed Systems Kun-Ming Yu *, Ching-Hsien Hsu and Chwani-Lii Sune Department of Computer Science and Information Engineering Chung-Hua

More information