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Evolutionary Computation

A Gentle Introduction

  • Chapter
Book cover Evolutionary Optimization

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 48))

Abstract

This chapter gives a gentle introduction to evolutionary computation, a field in which evolutionary optimisation is one of the most important research areas. Unlike most introductions to evolutionary computation which are based on its simplified biological link, this chapter emphasises the link between evolutionary computation and artificial intelligence and computer science. In fact, this whole book is centred around problem-solving, e.g., optimisation, using evolutionary computation techniques. It does not deal with the issue of biological modelling.

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References

  • Axelrod, R. (1987). The evolution of strategies in the iterated prisoner’s dilemma. in Genetic Algorithms and Simulated Annealing (L. Davis, ed.), ch.-3, San Mateo, CA: Morgan Kaufmann, 32–41.

    Google Scholar 

  • Back, T. (1996). Evolutionary Algorithms in Theory and Practice. New York: Oxford University Press.

    Google Scholar 

  • Bäck, T. and Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23.

    Article  Google Scholar 

  • Baker, J. E. (1985). Adaptive selection methods for genetic algorithms. in Proc. of an Int’l Conf. on Genetic Algorithms and Their Applications (J. J. Grefenstette, ed.), 101–111.

    Google Scholar 

  • Beyer, H.-G. (1994). Towards a theory of evolution strategies: the (μ, λ) theory. Evolutionary Computation, 2(4), 381–407.

    Article  MathSciNet  Google Scholar 

  • Colombetti, M. and Dorigo, M. (1998) Evolutionary computation in behavior engineering, in Evolutionary Computation, World Scientific Publ. Co.

    Google Scholar 

  • Darwen, P. J. and Lao, X. (1995). On evolving robust strategies for iterated prisoner’s dilemma, in Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, Vol. 956 (X. Lao, ed.), (Heidelberg, Germany), Springer-Verlag, 276–292.

    Google Scholar 

  • Darwen, P. J. and Yao, X. (1996). Automatic modularization by speciation. in Proc. of the 1996 IEEE Int’l Conf. on Evolutionary Computation (ICEC’96), Nagoya, Japan, IEEE Press, New York, NY 10017-2394, 88–93.

    Google Scholar 

  • Darwen, P. J. and Lao, X. (1997). Speciation as automatic categorical modularization. IEEE Transactions on Evolutionary Computation, 1(2), 101–108.

    Article  Google Scholar 

  • Davidor, Y. (1991). Epistasis variance: a viewpoint on GA-hardness. in Foundations of Genetic Algorithms (G. J. E. Rawlins, ed.), Morgan Kaufmann, San Mateo, CA, 23–35.

    Google Scholar 

  • Deb, K. and Goldberg, D. E. (1993). Analyzing deception in trap functions, in Foundations of Genetic Algorithms 2 (L. D. Whitley, ed.), (San Mateo, CA), Morgan Kaufmann, 93–108.

    Google Scholar 

  • de Garis, H. (1990). Genetic programming: modular evolution for Darwin machines, in Proc. of Int’l Joint Conf. on Neural Networks, Vol. I, (Washington, DC), Lawrence Erlbaum Associates, Hillsdale, NJ, 194–197,.

    Google Scholar 

  • DeJong, K. A. (1993). Genetic algorithms are NOT function optimizers. in Foundations of Genetic Algorithms 2 (L. D. Whitley, ed.), (San Mateo, CA), Morgan Kaufmann, 5–17.

    Google Scholar 

  • Eiben, A. E., Aarts, E. H. L. and van Hee, K. M. (1991). Global convergence of genetic algorithms: a markov chain analysis, in Parallel Problem Solving from Nature (H.-P. Schwefel and R. Manner, eds.), Springer-Verlag, Heidelberg, 4–12.

    Google Scholar 

  • Eshelman, L., Caruana, R. and Schaffer, J. D. (1989). Biases in the crossover landscape. in Proc. of the Third Int’l Conf. on Genetic Algorithms and Their Applications (J. D. Schaffer, ed.), Morgan Kaufmann, San Mateo, CA, 10–19.

    Google Scholar 

  • Fogel, D. B. (1998). An evolutionary approach to the traveling salesman problem. Biological Cybernetics, 60, 139–144.

    Article  MathSciNet  Google Scholar 

  • Fogel, D. B. (1991). System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Needham Heights, MA 02194: Ginn Press.

    Google Scholar 

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

    Google Scholar 

  • Fogel, D. B. (1994). An introduction to simulated evolutionary optimisation. IEEE Trans, on Neural Networks, 5(1), 3–14.

    Article  Google Scholar 

  • Fogel, D. B. (1995). Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. New York, NY: IEEE Press.

    Google Scholar 

  • Fogel, D. B. (1995a). A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems. Simulation, 64(6), 399–406.

    Article  Google Scholar 

  • Fogel, L. J., Owens, A. J. and Walsh, M. J. (1966). Artificial Intelligence Through Simulated Evolution. New York, NY: John Wiley & Sons.

    Google Scholar 

  • Fogel D. B. and Atmar, J. W. (1990). Comparing genetic operators with Gaussian mutations in simulated evolutionary process using linear systems. Biological Cybernetics, 63(2), 111–114.

    Article  Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1), 1–16.

    Article  Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms — part i: A unified formulation. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 28(1), 26–37.

    Article  Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1998a). Multiobjective optimization and multiple constraint handling with evolutionary algorithms — part ii: Application example. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 28(1), 38–47.

    Article  Google Scholar 

  • Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers. I. Introduction. Australian Journal of Biological Sciences, 10, 484–491.

    Google Scholar 

  • Garey M. R. and Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco: W. H. Freeman Co..

    Google Scholar 

  • Glover, F. (1989). Tabu search — part I. ORSA J. on Computing, 1, 190–206.

    MATH  MathSciNet  Google Scholar 

  • Glover, F. (1990). Tabu search — part II. ORSA J. on Computing, 2, 4–32.

    MATH  Google Scholar 

  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Goldberg, D. E. (1990). A note on Bltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems, 4, 445–460.

    MATH  Google Scholar 

  • Grefenstette, J. J. (1993). Deception considered harmful. in Foundations of Genetic Algorithms 2 (L. D. Whitley, ed.), (San Mateo, CA), Morgan Kaufmann, 75–91.

    Google Scholar 

  • Grefenstette, J. J., Gopal, R., Rosmaita, B. J. and van Gucht, D. (1985). Genetic algorithms for the traveling salesman problem. in Proc. of the First Int’l Conf. on Genetic Algorithms and Their Applications (J. J. Grefenstette, ed.), Carnegie-Mellon University, 160–168.

    Google Scholar 

  • Heckendorn R. B. and Whitley, D. (1997). A Walsh analysis of NK-landscapes. in Proc. of the 7th Int’l Conf. on Genetic Algorithms (T. Bäck, ed.), (San Francisco, CA), Morgan Kaufmann, 41–48.

    Google Scholar 

  • Hillis, W. D. (1991). Co-evolving parasites improve simulated evolution as an optimization procedure. in Santa Fe Institute Studies in the Sciences of Complexity, Volume-10, Addison-Wesley, 313–323.

    Google Scholar 

  • Hinterding, R. and Khan, L. (1995). Genetic algorithms for cutting stock problems: with and without contiguity. in Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, Vol. 956 (X. Lao, ed.), (Heidelberg, Germany), Springer-Verlag, 166–186.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press.

    Google Scholar 

  • Holland, J. H., Holyoak, K. J., Nisbett, R. E. and Thagard, P. R. (1986). Induction: Processes of Inference, Learning, and Discovery. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Holland, J. H. (1988). Using classifier systems to study adaptive nonlinear networks. in Lectures in the Sciences of Complexity (D. L. Stein, ed.), Redwood City, CA: Addison-Wesley, 463–499.

    Google Scholar 

  • Holland, J. H. (1992). Adaptation in Natural and Artificial Systems (1st MIT Press Edn). Cambridge, MA: The MIT Press.

    Google Scholar 

  • Hordijk, W. (1996). A measure of landscapes. Evolutionary Computation, 4(4), 335–360.

    Article  Google Scholar 

  • Ingber, L. (1989). Very fast simulated re-annealing. Mathl. Comput. Modelling, 12(8), 967–973.

    Article  MATH  MathSciNet  Google Scholar 

  • Jones, T. (1995). Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, The University of New Mexico, Albuquerque, New Mexico.

    Google Scholar 

  • Jong, K. A. D. (1975). An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor.

    Google Scholar 

  • Kappler, C. (1996). Are evolutionary algorithms improved by large mutations?. in Parallel Problem Solving from Nature (PPSN) IV (H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, eds.), vol. 1141 of Lecture Notes in Computer Science, (Berlin), Springer-Verlag, 346–355.

    Chapter  Google Scholar 

  • Kido, T., Takagi, K. and Nakanishi, M. (1994). Analysis and comparisons of genetic algorithm, simulated annealing, tabu search, and evolutionary combination algorithm. Informatica, 18, 399–410.

    Google Scholar 

  • Kim, J.-H. and Myung, H. (1997). Evolutionary programming techniques for constrained optimization problems. IEEE Transactions on Evolutionary Computation, 1(2), 129–140.

    Article  Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  ADS  MathSciNet  Google Scholar 

  • Koza, J. R. (1989). Evolving programs using symbolic expressions. in Sridharan,ed.), (San Mateo, CA), Morgan Kaufmann, 768–774.

    Google Scholar 

  • Koza, J. R. (1990). Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Tech. Rep. STAN-CS-90-1314, Department of Computer Science, Stanford University.

    Google Scholar 

  • Koza, J. R. (1992). Genetic Programming. Cambridge, Mass.: The MIT Press.

    Google Scholar 

  • Koza, J. and D. Andre, D. (1998). Automatic discovery of protein motifs using genetic programming, in Evolutionary Computation, World Scientific Publ. Co..

    Google Scholar 

  • Liang, K.-H., Yao, X., Newton, C. and Hoffman, D. (1998). Solving cutting stock problems by evolutionary programming. in Evolutionary Programming VII: Proc. of the 7th Annual Conference on Evolutionary Programming (V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, eds.), vol. 1447 of Lecture Notes in Computer Science, (Berlin), Springer-Verlag, 291–300.

    Google Scholar 

  • Lienig, J. and Thulasiraman, K. (1995). GASBOR: A genetic algorithm for switchbox routing in integrated circuits. in Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, Vol. 956 (X. Lao, ed.), (Heidelberg, Germany), Springer-Verlag, 187–200.

    Google Scholar 

  • Liepins G. E. and Vose, M. D. (1991). Deceptiveness and genetic algorithm dynamics. in Foundations of Genetic Algorithms (G. J. E. Rawlins, ed.), 1, (San Mateo, CA), Morgan Kaufmann, 36–50.

    Google Scholar 

  • Liu, Y. and Lao, X. (1996). A population-based learning algorithm which learns both architectures and weights of neural networks. Chinese Journal of Advanced Software Research (Allerton Press, Inc., New York, NY 10011), 3(1), 54–65.

    Google Scholar 

  • Luan, F. and Yao, X. (1994). Solving real-world lecture room assignment problems by genetic algorithms. in Complex Systems — From Local Interactions to Global Phenomena, IOS Press, Amsterdam, 148–160.

    Google Scholar 

  • Manderick, B., de Weger, M. and Spiessens, P. (1991). The genetic algorithm and the structure of the fitness landscape. in Proc. of the Fourth Int’l Conf. on Genetic Algorithms (R. K. Belew and L. B. Booker, eds.), Morgan Kaufmann, San Mateo, CA, 143–150.

    Google Scholar 

  • Mathias K. E. and Whitley, D. (1994). Changing representations during search: A comparative study of delta coding. Evolutionary Computation, 2(3), 249–278.

    Article  Google Scholar 

  • Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Germany: Springer-Verlag.

    Google Scholar 

  • Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs (3rd edition). Berlin, Germany: Springer-Verlag.

    Google Scholar 

  • Michalewicz, Z. and Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1), 1–32.

    Article  Google Scholar 

  • Reynolds, D. and Gomatam, J. (1996). Stochastic modelling of genetic algorithms. Artificial Intelligence, 82(1–2), 303–330.

    Article  MathSciNet  Google Scholar 

  • Rosin, C. D. and Belew, R. K. (1997). New methods for competitive coevolution. Evolutionary Computation, 5(1), 1–29.

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Rudolph, G. (1994a). Convergence of non-elitist strategies. in Proc. of the 1994 IEEE Int’l Conf. on Evolutionary Computation (ICEC’94) (Z. M. et al.,ed.), IEEE Press, New York, NY 10017-2394, 63–66.

    Google Scholar 

  • Rudolph, G. (1996). Convergence analysis of evolutionary algorithms in general search spaces, in Proc. of the 1996 IEEE Int’l Conf. on Evolutionary Computation (ICEC’96), (New York, NY), IEEE Press, 50–54.

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Smith, R. E. and Goldberg, D. E. (1992). Reinforcement learning with classifier systems: adaptive default hierarchy formation. Applied Artificial Intelligence, 6, 79–102.

    Article  Google Scholar 

  • Suzuki, J. (1993). A markov chain analysis on a genetic algorithm. in Proc. of the Fifth Int’l Conf. on Genetic Algorithms (S. Forrest, ed.), Morgan Kaufmann, San Mateo, CA, 146–153.

    Google Scholar 

  • Suzuki, J. (1995). A markov chain analysis on a simple genetic algorithm. IEEE Trans. on Systems, Man, and Cybernetics, 25, 655–659.

    Article  Google Scholar 

  • Szu, H. H. and Hartley, R. L. (1987). Fast simulated annealing. Physics Letters A, 122, 157–162.

    Article  ADS  Google Scholar 

  • Vignaux, G. A. and Michalewicz, Z. (1991). A genetic algorithm for the linear transportation problem. IEEE Trans, on Systems, Man, and Cybernetics, 21(2), 445–452.

    Article  MathSciNet  Google Scholar 

  • Westerdale, T. H. (1997). Classifier systems — no wonder they don’t work. in Proc. of the 2nd Annual Conf. on Genetic Programming (J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, eds.), (San Francisco, CA), Morgan Kaufmann, 529–537.

    Google Scholar 

  • Whitley, D., Starkweather, T. and Shaner, D. (1991). The traveling salesman and sequence scheduling: quality solutions using genetic edge recombination. in Handbook of Genetic Algorithms (L. Davis, ed.), ch. 22, New York, NY: Van Nostrand Reinhold, 350–372.

    Google Scholar 

  • Wilson, S. W. (1995). Classifier fitness based on accuracy. Evolutionary Computation, 3(2), 149–175.

    Article  Google Scholar 

  • Yao, X. (1991). Optimization by genetic annealing, in Proc. of Second Australian Conf. on Neural Networks (M. Jabri, ed.), (Sydney, Australia), 94–97.

    Google Scholar 

  • Yao, X. (1991a). Simulated annealing with extended neighbourhood. Int. J. of Computer Math., 40, 169–189.

    Article  MATH  Google Scholar 

  • Yao, X. (1991b). Evolution of connectionist networks, in Preprints of the Int’l Symp. on AI, Reasoning & Creativity (T. Dartnall, ed.), (Queensland, Australia), Griffith University, 49–52.

    Google Scholar 

  • Yao, X. (1993). An empirical study of genetic operators in genetic algorithms. Microprocessing and Microprogramming, 38, 707–714.

    Article  Google Scholar 

  • Yao, X. (1993a). A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8(4), 539–567.

    Article  Google Scholar 

  • Yao, X. (1995). A new simulated annealing algorithm. Int. J. of Computer Math., 56, 161–168.

    Article  MATH  Google Scholar 

  • Yao, X. (1995a). Evolutionary artificial neural networks. in Encyclopedia of Computer Science and Technology (A. Kent and J. G. Williams, eds.), vol.-33, New York, NY 10016: Marcel Dekker Inc., 137–170.

    Google Scholar 

  • Yao, X. and Darwen, P. (1994). An experimental study of N-person iterated prisoner’s dilemma games. Informatica, 18, 435–450.

    Google Scholar 

  • Yao, X. and Liu, Y. (1996). Fast evolutionary programming. in Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming (L. J. Fogel, P. J. Angeline, and T. Back, eds.), (Cambridge, MA), The MIT Press, 451–460.

    Google Scholar 

  • Yao, X. and Liu, Y. (1997). Fast evolution strategies. Control and Cybernetics, 26(3), 467–496.

    MathSciNet  Google Scholar 

  • Yao, X. and Liu, Y. (1997a). A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8(3), 694–713.

    Article  MathSciNet  Google Scholar 

  • Yao, X. and Liu, Y. (1998). Making use of population information in evolutionary artificial neural networks. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 28(3), 417–425.

    Google Scholar 

  • Yao, X. and Shi, Y. (1995). A preliminary study on designing artificial neural networks using co-evolution. in Proc. of the IEEE Singapore Intl Conf on Intelligent Control and Instrumentation, (Singapore), IEEE Singapore Section, 149–154.

    Google Scholar 

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© 2003 Kluwer Academic Publishers

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Yao, X. (2003). Evolutionary Computation. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol 48. Springer, Boston, MA. https://doi.org/10.1007/0-306-48041-7_2

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  • DOI: https://doi.org/10.1007/0-306-48041-7_2

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