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An analysis of the ideal abstract genetic algorithm package, and an evaluation of a specific package in relation to these criteria, with specific focus on its suitability as a teaching tool

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An analysis of the ideal abstract genetic algorithm package, and an evaluation of a specific package in relation to these criteria, with specific focus on its suitability as a teaching tool Matthew W. A. Sparkes BSc School of Computing Sciences, University of East Anglia, Norwich, United Kingdom, NR4 7TJ matt.sparkes@gmail.com April 6, 2006 Abstract This paper will clearly outline what a genetic algorithm is, and in what capacity they have been utiliised to solve real world problems. Taking th
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  An analysis of the ideal abstract geneticalgorithm package, and an evaluation of aspecific package in relation to these criteria,with specific focus on its suitability as ateaching tool Matthew W. A. Sparkes BScSchool of Computing Sciences,University of East Anglia,Norwich,United Kingdom,NR4 7TJ  matt.sparkes@gmail.com April 6, 2006 Abstract This paper will clearly outline what a genetic algorithm is, and inwhat capacity they have been utiliised to solve real world problems.Taking this information it will then examine the set of features thatwould be present in the ideal genetic algorithm package, before exam-ining a specific package against these criteria to assess its suitabilityas a teaching tool. 1  Contents 1 Awknowledgements 42 What is a Genetic Algorithm? 5 2.1 Natural Selection and Mutation in Nature . . . . . . . . . . . 62.2 Evolution as a Paradigm for Problem Solving . . . . . . . . . 7 3 Basic Elements of a GA 8 3.1 Encoding and Population Size . . . . . . . . . . . . . . . . . . 83.2 Crossover Operations . . . . . . . . . . . . . . . . . . . . . . . 93.2.1 Single Point Crossover . . . . . . . . . . . . . . . . . . 93.2.2 Double Point Crossover . . . . . . . . . . . . . . . . . . 93.2.3 Cut and Splice Crossover . . . . . . . . . . . . . . . . . 93.2.4 Uniform Crossover . . . . . . . . . . . . . . . . . . . . 103.3 Mutation Operations . . . . . . . . . . . . . . . . . . . . . . . 103.4 Fitness Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 103.5 Halting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.5.1 Resource Based Halting . . . . . . . . . . . . . . . . . 113.5.2 Solution Fitness Based Halting . . . . . . . . . . . . . 113.5.3 Progress Based Halting . . . . . . . . . . . . . . . . . . 12 4 Background Information on GAs 13 4.1 The Birth of GA . . . . . . . . . . . . . . . . . . . . . . . . . 134.2 Problems Associated with GA/GP . . . . . . . . . . . . . . . 134.2.1 Complexity and Reliability . . . . . . . . . . . . . . . . 13 5 Current Applications of Genetic Algorithms 15 5.1 Electrical Circuit Design . . . . . . . . . . . . . . . . . . . . . 155.2 The Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155.3 Gene Sequence Analysis . . . . . . . . . . . . . . . . . . . . . 16 6 Package Chosen for Assessment 177 Evaluation of Existing GA Package 17 7.1 Ease of Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.2 Problem Customisation . . . . . . . . . . . . . . . . . . . . . . 177.2.1 getValue Method . . . . . . . . . . . . . . . . . . . . . 187.2.2 draw Method . . . . . . . . . . . . . . . . . . . . . . . 182  7.2.3 createAllelesMap Method . . . . . . . . . . . . . . . . 187.3 Encoding and Population Size . . . . . . . . . . . . . . . . . . 187.4 Mutation and Crossover Operators . . . . . . . . . . . . . . . 197.4.1 Mutation Operations . . . . . . . . . . . . . . . . . . . 197.4.2 Crossover Operations . . . . . . . . . . . . . . . . . . . 197.5 Special GA Mechanisms . . . . . . . . . . . . . . . . . . . . . 207.5.1 Automatic Kick . . . . . . . . . . . . . . . . . . . . . . 207.5.2 Kin Competition Compensation . . . . . . . . . . . . . 207.5.3 Generational Memory . . . . . . . . . . . . . . . . . . 217.5.4 Pre and Post Breed Processing . . . . . . . . . . . . . 217.6 Fitness Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 227.7 Halting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 8 Conclusion and Evaluation 23 8.1 Suitability of GA Playground as a Teaching Tool . . . . . . . 238.1.1 Limitations of GA Playground . . . . . . . . . . . . . . 238.1.2 Benefits of GA Playground . . . . . . . . . . . . . . . . 238.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 9 Appendix 29 9.1 Example GA Playground Input File . . . . . . . . . . . . . . . 299.2 GA Playground Mutation Function . . . . . . . . . . . . . . . 319.3 GA Playground Crossover Function . . . . . . . . . . . . . . . 339.4 Screenshots . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353  1 Awknowledgements Project supervisor Professor VJ Rayward-Smith.4
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