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An Introduction to Evolutionary Algorithms

  • Chapter
Estimation of Distribution Algorithms

Part of the book series: Genetic Algorithms and Evolutionary Computation ((GENA,volume 2))

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Abstract

In this first chapter an introduction to Evolutionary Algorithms will be given. The introduction is focused on optimization. The basic components of the most used Evolutionary Algorithms —Genetic Algorithms, Evolution Strategies and Evolutionary Programming— are explained in detail. We give pointers to the literature on their theoretical foundations.

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References

  • Ackley, D. H. (1987). A Connectionist Machine for Genetic Hillclimbing. Kluwer Academic Press.

    Book  Google Scholar 

  • Bäck, T. (1993). Optimal mutation rates in genetic search. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 2–9. Morgan Kaufmann Publishers.

    Google Scholar 

  • Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press.

    Google Scholar 

  • Bäck, T., Hammel, U., and Schwefel, H.-P. (1997). Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):3–17.

    Article  Google Scholar 

  • Bäck, T., Rudolph, G., and Schwefel, H.-P. (1993). Evolutionary programming and evolution strategies: similarities and differences. In Fogel, D. B. and Atmar, W., editors, Proceedings of the Second Annual Conference on Evolutionary Programming, pages 11–22. Evolutionary Programming Society.

    Google Scholar 

  • Bäck, T. and Schwefel, H.-P. (1996). Evolutionary computation: An overview. In Proceedings of the Third IEEE Conference on Evolutionary Computation, pages 20–29. IEEE press.

    Google Scholar 

  • Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. In Grefenstette, J. J., editorProceedings of the Second International Conference on Genetic Algorithms and Their Applications, pages 14–21.Lawrence Erlbaum Associates.

    Google Scholar 

  • Beyer, H.-G. (1995a). Toward a theory of evolution strategies: On the benefitsof sex-the (phi, A) theory. Evolutionary Computation, 3(1):81–111.

    Article  MathSciNet  Google Scholar 

  • Beyer, H.-G. (1995b). Toward a theory of evolution strategies: The (A, A)-theory. Evolutionary Computation, 2(4):381–407.

    Article  Google Scholar 

  • Beyer, H.-G. (1996). Toward a theory of evolution strategies: Self-adaptation. Evolutionary Computation, 3(3):311–347.

    Article  Google Scholar 

  • Bridges, C. L. and Goldberg, D. E. (1987). An analysis of reproduction and crossover in a binary-coded genetic algorithm. In Grefenstette, J. J., editor, Proceedings of the Second International Conference on Genetic Algorithms,pages 9–13. Lawrence Erlbaum Associates.

    Google Scholar 

  • Brindle, A. (1991). Genetic algorithm for function optimization. Doctoral Dissertation, University of Alberta.

    Google Scholar 

  • Darwin, C. (1859). The Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Mentor Reprint, 1958, New York.

    Google Scholar 

  • Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 61–69. Morgan Kaufmann.

    Google Scholar 

  • Davis, T. E. and Principe, J. C. (1993). A Markov chain framework for thesimple genetic algorithm. Evolutionary Computation, 1(3):269–288.

    Article  Google Scholar 

  • De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptivesystems. Doctoral Dissertation. University of Michigan.

    Google Scholar 

  • De Jong, K. A., Spears, W. M., and Gordon, D. F. (1995). Using Markov chains to analyze GAFOs. In Whitley, D. and Vose, M. D., editors,Foundations of Genetic Algorithms 3, pages 115–138. Morgan Kaufmann.

    Google Scholar 

  • Devorye, L. P. (1976). On the convergence of statistical search. IEEE Transactions on Systems,Man, and Cybernetics, 6(1):46–56.

    Google Scholar 

  • Eiben, A. E., Aarts, E. H. L., and Hee, K. M. V. (1991). Global convergence of genetic algorithms: A Markov chain analysis. In Schwefel, H.-P. and Manner, R., editors, Parallel Problem Solving from Nature,PPSN I. Lectures Notes in Computer Science,volume 496, pages 4–12. Springer Verlag.

    Google Scholar 

  • Fogarty, T. C. (1989). Varying the probability of mutation in the genetic algorithm. In Schaffer, J. D., editor,Proceedings of the Third International Conference on Genetic Algorithms, pages 104–109. Morgan Kaufmann.

    Google Scholar 

  • Fogel, D. B. (1992). Evolving Artificial Intelligence. PhD Thesis, University of California, San Diego, CA.

    Google Scholar 

  • Fogel, D. B. (1994). An introduction to evolutionary computation. IEEE Transactions on Neural Networks, 5(1):3–14.

    Article  Google Scholar 

  • Fogel, D. B. (1995). Evolutionary Computation: toward a new philosophy of machine intelligence. IEEE Press, Piscataway, New Jersey.

    Google Scholar 

  • Fogel, D. B. (1998). Evolutionary Computation. The Fossil Record. IEEE press. Fogel, L. J. (1962). Autonomous automata. Industrial Research, 4:14–19.

    Google Scholar 

  • Fogel, L. J. (1964). On the Organization of Intellect. Doctoral Dissertation.University of California, Los Angeles, CA.

    Google Scholar 

  • Goldberg, D. E. (1989).Genetic algorithms in search, optimization,and machine learning. Addison-Wesley.

    Google Scholar 

  • Goldberg, D. E. (1998). The Race, the Hurdle, and the Sweet Spot: Lessons from Genetic Algorithms for the Automation of Design Innovation and Creativity. Technical Report I11iGAL Report No. 98007, University of Illinois at Urbana-Champaign.

    Google Scholar 

  • Goldberg, D. E. and Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Rawlins, G. J. E., editor, Foundations of Genetic Algorithms,pages 69–93. Morgan Kaufmann.

    Google Scholar 

  • Grefenstette, J. J. (1986), Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems,Man, and Cybernetics, 16(1):122–128.

    Article  Google Scholar 

  • He, J. and Kang, L. (1999). On the convergence rates of genetic algorithms. Theoretical Computer Science, 229(1–2):23–39.

    Article  MathSciNet  MATH  Google Scholar 

  • Hesser, J. and Manner, R. (1990). Towards an optimal mutation probability for genetic algorithms. In Parallel Problem Solving from Nature, PPSN I.

    Google Scholar 

  • Lectures Notes in Computer Science, volume 496, pages 23–32. Springer-Verlag.

    Google Scholar 

  • Höffmeister, F. and Bäck, T. (1991). Genetic algorithms and evolution strategies: Similarities and differences. In Schwefel, H.-P. and Manner, R., editors,Parallel Problem Solving from Nature,PPSN I. Lectures Notes in Computer Science, volume 496, pages 455–470. Springer.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI.

    Google Scholar 

  • Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.

    Google Scholar 

  • Larrañaga, P., Kuijpers, C. M. H., Murga, R. H., Inza, I., and Dizdarevic, S. (1999). Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13:129–170.

    Article  Google Scholar 

  • Lozano, J. A., Larrañaga, P., Albizuri, F. X., and Graña, M. (1999). Genetic algorithms: Bridging the convergence gap. Theoretical Computer Science, 229(1–2):11–22.

    Article  MathSciNet  MATH  Google Scholar 

  • Lozano, J. A., Larrañaga, P., and Graña, M. (1998). Partitional cluster analysis with genetic algorithms: searching for the number of clusters. In Hayashi, C., Ohsumi, N., Yajima, K., Tanaka, Y., Bock, H. H., and Baba, Y., editors, Data Science, Classification and Related Methods, pages 117–125. Springer.

    Google Scholar 

  • Mahfoud, S. W. (1993). Finite Markov chain models of an alternative selection strategy for the genetic algorithm. Complex Systems, 7:155–170.

    MATH  Google Scholar 

  • Michalewicz, Z. and Janikov, C. Z. (1991). Handling constraints in genetic algorithms. In Belew, R. and Booker, L. B., editors,Proceedings of the Fourth International Conference on Genetic Algorithms,pages 151–157. Morgan Kaufmann.

    Google Scholar 

  • MĂĽhlenbein, H. (1992). How genetic algorithms really work. I: Mutation and hillclimbing. In Manner, R. and Manderick, B., editors,Parallel Problem Solving from Nature II,pages 15–25. North-Holland.

    Google Scholar 

  • MĂĽhlenbein, H. and Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm. I: Continuous parameter optimization. Evolutionary Computation, 1(1):25–49.

    Article  Google Scholar 

  • Nix, A. E. and Vose, M. D. (1992). Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 5:79–88.

    Article  MathSciNet  MATH  Google Scholar 

  • Oyman, A. I. and Beyer, H.-G. (2000). Analysis of (µ/.z, A)-ES on the parabolic ridge. Evolutionary Computation, 8(3):267–289.

    Article  Google Scholar 

  • Oyman, A. I., Beyer, H.-G., and Schwefel, H.-P. (2000). Analysis of (1, A)-ES on the parabolic ridge. Evolutionary Computation, 8(3):249–265.

    Article  Google Scholar 

  • PintĂ©r, J. (1984). Convergence properties of stochastic optimization procedures. Mathematische Operationsforschung and Statisk, Series Optimization, 15:53–61.

    Google Scholar 

  • PrĂĽgel-Bennet, A. and Shapiro, J. L. (1997). The dynamics of genetic algorithms in simple random Ising systems. Physica D, 104(1):75–114.

    Article  MathSciNet  Google Scholar 

  • Rechenberg, I. (1973). Evolutionstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Fromman-Holzboog, Stuttgart.

    Google Scholar 

  • Reeves, C. R. (1993). Modern Heuristic Techniques for Combinatorial Optimization. Blackwell Scientific Publications.

    Google Scholar 

  • Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computer & Operations Research,22:5–13.

    Article  MATH  Google Scholar 

  • Rudolph, G. (1992). On correlated mutations in evolution strategies. In Manner, R. and Manderick, B., editors, Parallel Problem Solving from Nature, PPSN II, pages 105–114. North-Holland.

    Google Scholar 

  • Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1):96–101.

    Article  Google Scholar 

  • Rudolph, G. (1997). Convergence Properties of Evolutionary Algorithms. Kovac, Hamburg.

    Google Scholar 

  • Rudolph, G. (1998). Finite Markov chain results in evolutionary computation: A tour d’horizon. Fundamenta Informaticae, 35(1–4):67–89.

    MathSciNet  MATH  Google Scholar 

  • Schaffer, J. D., Caruana, R. A., Eshelman, L. J., and Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization.In Schaffer, J. D.,editor,Proceedings ofthe Third International Conference on Genetic Algorithms, pages 51–60. Morgan Kaufmann.

    Google Scholar 

  • Schwefel, H.-P. (1981). Numerical Optimization of Computer Models. John Wiley & Sons, Inc.

    Google Scholar 

  • Schwefel, H.-P. (1995). Evolution and Optimum Seeking. John Wiley & Sons, Inc.

    Google Scholar 

  • Solis, F. J. and Wets, R. J.-B. (1981). Minimization by random search techniques. Mathematics of Operations Research, 6:19–30.

    Article  MathSciNet  MATH  Google Scholar 

  • Suzuki, J. (1995). A Markov chain analysis on simple genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 25(4):655–659.

    Article  Google Scholar 

  • Syswerda, G. (1991). Schedule optimization using genetic algorithms. In Davis, L., editor,Handbook of Genetic Algorithms,pages 332–349. Van Nostrand Reinhold.

    Google Scholar 

  • Syswerda, G. (1993). Simulated crossover in genetic algorithms. In Whitley, L. D., editor, Foundations of Genetic Algorithms 2,pages 239–255. Morgan Kaufmann.

    Google Scholar 

  • van Nimwegen, E., Crutchfield, J. P., and Mitchell, M. (1999). Statistical dynamics of the royal road genetic algorithms. Theoretical Computer Science, 229(1–2):41–102.

    Article  MathSciNet  MATH  Google Scholar 

  • Vose, M. D. (1999). The simple genetic algorithm: foundations and theory. MIT Press.

    Google Scholar 

  • Vose, M. D. and Liepins, G. E. (1991). Punctuated equilibria in genetic search.Complex Systems,5:31–44.

    MathSciNet  MATH  Google Scholar 

  • Whitley, D. and Kauth, J. (1988). GENITOR: A different genetic algorithm.In Proceedings of the Rocky Mountain Conference on Artificial Intelligence, volume II, pages 118–130.

    Google Scholar 

  • Whitley, L. D. (1992). An executable model of a simple genetic algorithm. In Whitley, D., editor,Foundations of Genetic Algorithms 2,pages45–62. Morgan Kauffmann.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain

    J. A. Lozano

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  1. J. A. Lozano
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Editors and Affiliations

  1. University of the Basque Country, Spain

    Pedro Larrañaga  & Jose A. Lozano  & 

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Lozano, J.A. (2002). An Introduction to Evolutionary Algorithms. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_1

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  • DOI: https://doi.org/10.1007/978-1-4615-1539-5_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5604-2

  • Online ISBN: 978-1-4615-1539-5

  • eBook Packages: Springer Book Archive

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