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Computational Complexity pp 1309–1320Cite as

Genetic and Evolutionary Algorithms and Programming: General Introduction and Application to Game Playing

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Article Outline

Glossary

Definition of the Subject

Introduction

Evolutionary Algorithms

A Touch of Theory

Extensions of the Basic Methodology

Lethal Applications

Evolutionary Games

Future Directions

Bibliography

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Notes

  1. 1.

    Languages other than LISP have been used,although LISP is still by far the most popular within the geneticprogramming domain.

  2. 2.

    The highest‐ranking playerwe consulted was Boris Gutkin, ELO 2400, International Master, andfully qualified chess teacher.

Abbreviations

Evolutionary algorithms /evolutionary computation:

A family of algorithms inspired by the workings of evolution by naturalselection whose basic structure is to:

  1. 1.

    produce an initial population of individuals, these latterbeing candidate solutions to the problem at hand

  2. 2.

    evaluate the fitness of each individual in accordance with theproblem whose solution is sought

  3. 3.

    while termination condition not met do

    1. (a)

      select fitter individuals for reproduction

    2. (b)

      recombine (crossover) individuals

    3. (c)

      mutate individuals

    4. (d)

      evaluate fitness of modified individuals

    end while

Genome/chromosome:

An individual's makeup in the population ofan evolutionary algorithm is known as a genome, or chromosome. It cantake on many forms, including bit strings, real‐valued vectors,character‐based encodings, and computer programs. The representationissue – namely, defining an individual's genome (well) – is criticalto the success of an evolutionary algorithm.

Fitness:

A measure of the quality of a candidate solution in thepopulation. Also known as fitness function . Defining thisfunction well is critical to the success of an evolutionary algorithm.

Selection:

The operator by which an evolutionary algorithmselects (usually probabilistically) higher‐fitness individuals tocontribute genetic material to the next generation.

Crossover:

One of the two main genetic operators applied byan evolutionary algorithm, wherein two (or more) candidate solutions(parents) are combined in some pre‐defined manner to formoffspring.

Mutation:

One of the two main genetic operators applied byan evolutionary algorithm, wherein one candidate solution is randomlyaltered.

Bibliography

  1. Abelson H, Sussman GJ, Sussman J (1996) Structure and Interpretation of ComputerPrograms, 2nd edn. MIT Press, Cambridge

    MATH  Google Scholar 

  2. Azaria Y, Sipper M (2005) GP‐Gammon: Genetically programmingbackgammon players. Genet Program Evolvable Mach 6(3):283–300. doi:10.1007/s10710-005-2990-0

    Article  Google Scholar 

  3. Azaria Y, Sipper M (2005) GP‐Gammon: Using genetic programming to evolve backgammon players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert J, Tomassini M (eds) Proceedings of 8th European Conference on Genetic Programming (EuroGP2005). Lecture Notes in Computer Science, vol 3447. Springer, Heidelberg, pp 132–142. doi:10.1007/b107383

    Google Scholar 

  4. Bain M (1994) Learning logical exceptions in chess. Ph D thesis, University ofStrathclyde, Glasgow, Scotland.citeseer.ist.psu.edu/bain94learning.html

  5. Bonanno G (1989) The logic of rational play in games of perfect information.Papers 347, California Davis – Institute of Governmental Affairs.http://ideas.repec.org/p/fth/caldav/347.html

  6. Charness N (1991) Expertise in chess: The balance between knowledge and search.In: Ericsson KA, Smith J (eds) Toward a general theory of Expertise:Prospects and limits. Cambridge University Press, Cambridge

    Google Scholar 

  7. Cramer NL (1985) A representation for the adaptive generation of simplesequential programs. In: Grefenstette JJ (ed) Proceedings of the 1stInternational Conference on Genetic Algorithms. Lawrence Erlbaum Associates, Mahwah, pp 183–187

    Google Scholar 

  8. Epstein SL (1999) Game playing: The next moves. In: Proceedings of theSixteenth National Conference on Artificial Intelligence. AAAI Press, MenloPark, pp 987–993

    Google Scholar 

  9. Fogel DB (2006) Evolutionary Computation: Toward a New Philosophy of MachineIntelligence, 3rd edn. Wiley-IEEE Press, Hoboken

    Google Scholar 

  10. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial Intelligence Through SimulatedEvolution. Wiley, New York

    MATH  Google Scholar 

  11. Fürnkranz J (1996) Machine learning in computer chess: The next generation.Int Comput Chess Assoc J 19(3):147–161.citeseer.ist.psu.edu/furnkranz96machine.html

  12. Hauptman A, Sipper M (2005) Analyzing the intelligence ofa genetically programmed chess player. In: Late Breaking Papers at the 2005Genetic and Evolutionary Computation Conference, distributed onCD-ROM at GECCO-2005, Washington DC

    Google Scholar 

  13. Hauptman A, Sipper M (2005) GP‐EndChess: Using genetic programming to evolve chessendgame players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert J, Tomassini M (eds)Proceedings of 8th European Conference on Genetic Programming (EuroGP2005). Lecture Notes inComputer Science, vol 3447. Springer, Heidelberg, pp 120–131. doi:10.1007/b107383

    Google Scholar 

  14. Hauptman A, Sipper M (2007) Emergence of complex strategies inthe evolution of chess endgame players. Adv Complex Syst 10(1):35–59. doi:10.1142/s0219525907001082

    Article  MATH  Google Scholar 

  15. Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-n problem in chess. In: Ebner M, O'Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of 10th European Conference on Genetic Programming (EuroGP2007). Lecture Notes in Computer Science vol. 4455. Springer, Heidelberg, pp 78–89. doi:10.1007/978-3-540-71605-1_8

    Google Scholar 

  16. Holland JH (1975) Adaptation in Natural and ArtificialSystems: An Introductory Analysis with Applications to Biology,Control, and Artificial Intelligence.University of Michigan Press,Ann Arbor (2nd edn. MIT Press, Cambridge, 1992)

    Google Scholar 

  17. Holland JH (1992) Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  18. Kilinç S, Jain V, Aggarwal V, Cam U (2006) Catalogue ofvariable frequency and single‐resistance‐controlled oscillatorsemploying a single differential difference complementary currentconveyor. Frequenz: J RF‐Eng Telecommun 60(7–8):142–146

    Google Scholar 

  19. Koza JR (1992) Genetic Programming: On the Programming of Computers by Means ofNatural Selection. MIT Press, Cambridge

    MATH  Google Scholar 

  20. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) GeneticProgramming IV: Routine Human‐Competitive Machine Intelligence. Kluwer, Norwell

    MATH  Google Scholar 

  21. Laird JE, van Lent M (2000) Human‐level AI's killer application: Interactivecomputer games. In: AAAI-00: Proceedings of the 17th National Conference onArtificial Intelligence. MIT Press, Cambridge, pp 1171–1178

    Google Scholar 

  22. Lohn JD, Hornby GS, Linden DS (2005) An evolved antenna for deployment onNASA's Space Technology 5 mission. In: O'Reilly UM, Yu T, Riolo R, WorzelB (eds) Genetic Programming Theory and Practice II, Genetic Programming,vol 8, chap 18. Springer, pp 301–315. doi:10.1007/0-387-23254-0_18

    Google Scholar 

  23. Montana DJ (1995) Strongly typed genetic programming. Evol Comput3(2):199–230. doi:10.1162/evco.1995.3.2.199

    Article  Google Scholar 

  24. Peña-Reyes CA, Sipper M (2001) Fuzzy CoCo:A cooperative‐coevolutionary approach to fuzzy modeling. IEEE TransFuzzy Syst 9(5):727–737. doi:10.1009/91.963759

    Google Scholar 

  25. Preble S, Lipson M, Lipson H (2005) Two‐dimensional photonic crystals designedby evolutionary algorithms. Appl Phys Lett 86(6):061111. doi:10.1063/1.1862783

    Article  Google Scholar 

  26. Schwefel HP (1995) Evolution and Optimum Seeking. Wiley, New York

    Google Scholar 

  27. Shichel Y, Ziserman E, Sipper M (2005) GP‐Robocode: Using genetic programmingto evolve robocode players. In: Keijzer M, Tettamanzi A, Collet P, van HemertJ, Tomassini M (eds) Genetic Programming: 8th European Conference, EuroGP2005, Lausanne, Switzerland, March 30–April 1, 2005. Lecture Notes inComputer Science, vol 3447. Springer, Berlin, pp 143–154. doi:10.1007/b107383

    Google Scholar 

  28. Sipper M (2002) Machine Nature: The Coming Age of Bio‐Inspired Computing.McGraw‐Hill, New York

    Google Scholar 

  29. Sipper M, Azaria Y, Hauptman A, Shichel Y (2007) Designing an evolutionary strategizingmachine for game playing and beyond. IEEE Trans Syst, Man, Cybern,Part C: Appl Rev 37(4):583–593

    Google Scholar 

  30. Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmentingtopologies. Evol Comput 10(2):99–127. doi:10.1162/106365602320169811

    Article  Google Scholar 

  31. Turing AM (1950) Computing machinery and intelligence. Mind 59(236):433–460.http://links.jstor.org/sici?sici=0026-4423(195010)2:59:236<433:CMAI>2.0.CO;2-5

    Article  MathSciNet  Google Scholar 

  32. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEETrans Evol Comput 1(1):67–82. doi:10.1109/4235.585893

    Article  Google Scholar 

  33. Yao X (1999) Evolving artificial neural networks. Proc IEEE87(9):1423–1447. doi:10.1009/5.784219

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Ben‐Gurion University, Beer-Sheva, Israel

    Michael Orlov, Moshe Sipper & Ami Hauptman

Authors
  1. Michael Orlov
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  2. Moshe Sipper
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Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Orlov, M., Sipper, M., Hauptman, A. (2012). Genetic and Evolutionary Algorithms and Programming: General Introduction and Application to Game Playing. In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_81

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