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Evolutionary programming: an introduction and some current directions

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Abstract

Evolutionary programming was originally proposed in 1962 as an alternative method for generating machine intelligence. This paper reviews some of the early development of the method and focuses on three current avenues of research: pattern discovery, system identification and automatic control. Recent efforts along these lines are described. In addition, the application of evolutionary algorithms to autonomous system design on parallel processing computers is briefly discussed.

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References

  • Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 177–186.

    Google Scholar 

  • Atmar, J. W. (1976) Speculation on the Evolution of Intelligence and Its Possible Realization in Machine Form. Doctoral dissertation, New Mexico State University, Las Cruces.

    Google Scholar 

  • Bäck, T. and Hoffmeister, F. (1994) Basic aspects of evolution strategies. This issue.

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

    Google Scholar 

  • Barto, A. G., Sutton, R. S. and Anderson, C. W. (1983) Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics, 13, 834–846.

    Google Scholar 

  • Bremermann, H. J. (1962) Optimization through evolution and recombination. In Self-Organizing Systems, eds. M. C. Yovits, G. T. Jacobi, and G. D. Goldstine pp. 93–106. Spartan Books, Washington D.C.

    Google Scholar 

  • Burgin, G. H. (1969) On playing two-person zero-sum games against nonminimax players. IEEE Transactions on Systems Science and Cybernetics, 5, 369–370.

    Google Scholar 

  • Burgin, G. H. (1974) System identification by quasilinearization and evolutionary programming. Journal of Cybernetics, 2, 4–23.

    Google Scholar 

  • Caines, P. E. (1988) Linear Stochastic Systems. John Wiley, New York.

    Google Scholar 

  • Conrad, M. (1974) Evolutionary learning circuits. Journal of Theoretical Biology, 46, 167–188.

    Google Scholar 

  • Conrad, M. (1990) The geometry of evolution. BioSystems, 24, 61–81.

    Google Scholar 

  • Dearholt, D. W. (1976) Some experiments on generalization using evolving automata. Ninth International Conference on System Sciences, Honolulu, HI, pp. 131–133.

  • Flood, M. M. (1962) Stochastic learning theory applied to chance experiments with cats, dogs, and men. Behavioral Science, 7, 289–314.

    Google Scholar 

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

    Google Scholar 

  • Fogel, D. B. (1992a) Using evolutionary programming for modeling: an ocean acoustic example. IEEE Journal of Oceanic Engineering, 17, 333–340.

    Google Scholar 

  • Fogel, D. B. (1992b) Evolving Artificial Intelligence. Doctoral Dissertation, UCSD.

  • Fogel, D. B. (1993a) On the philosophical differences between evolutionary algorithms and genetic algorithms. Proceedings of the Second Annual Conference on Evolutionary Programming, eds. D. B. Fogel and W. Atmar pp. 23–29. Evolutionary Programming Society, La Jolla, CA.

    Google Scholar 

  • Fogel, D. B. (1993b) Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems, 24, 27–36.

    Google Scholar 

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

    Google Scholar 

  • Fogel, D. B. and Atmar, W. (eds) (1992) Proceedings of the First Annual Conference on Evolutionary Programming. Evolutionary Programming Society, La Jolla, CA.

    Google Scholar 

  • Fogel, D. B. and Atmar, W. (eds) (1993) Proceedings of the Second Annual Conference on Evolutionary Programming, Evolutionary Programming Society, La Jolla, CA.

    Google Scholar 

  • Fogel, D. B., Fogel, L. J. and Porto, V. W. (1990). Evolving neural networks. Biological Cybernetics, 63, 487–493.

    Google Scholar 

  • Fogel, D. B. and Simpson, P. K. (1993a) Evolving fuzzy clusters. In Proceedings of 1993 IEEE International Conference on Neural Networks, pp. 1829–1834. San Francisco, CA.

  • Fogel, D. B. and Simpson, P. K. (1993b) Experiments with evolving fuzzy clusters. In Proceedings of the Second Annual Conference on Evolutionary Programming, eds. D. B. Fogel and W. Atmar, pp. 90–97. Evolutionary Programming Society, La Jolla, CA.

    Google Scholar 

  • Fogel, D. B. and Stayton, L. C. (1994) On the effectiveness of crossover in simulated evolutionary optimization. Bio-Systems. To appear.

  • Fogel, L. J. (1962) Autonomous automata. Industrial Research, 4, 14–19.

    Google Scholar 

  • Fogel, L. J. (1964) On the Organization of Intellect, Doctoral Dissertation, UCLA.

  • Fogel, L. J. (1968) Extending communication and control through simulated evolution. In Bioengineering—An Engineering View, Proceedings of Symposium on Engineering Significance of the Biological Sciences, ed. G. Bugliarello, pp. 286–304. San Francisco Press, San Francisco, CA.

    Google Scholar 

  • Fogel, L. J., Owens, A. J. and Walsh, M. J. (1964) An evolutionary prediction technique, International Conference on Microcircuit Theory and Information Theory, Tokyo, Japan, pp. 173–174. IEEE Press.

    Google Scholar 

  • Fogel, L. J., Owens, A. J. and Walsh, M. J. (1966) Artificial Intelligence through Simulated Evolution. John Wiley, New York.

    Google Scholar 

  • Fogel, L. J. and Burgin, G. H. (1969) Competitive goal-seeking through evolutionary programming. Final report under Contract No. AF 19(628)-5927, Air Force Cambridge Research Labs.

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

    Google Scholar 

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

    Google Scholar 

  • Ljung, L. (1987) System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Lutter, B. E. and Huntsinger, R. C. (1969) Engineering applications of finite automata. Simulation, 13, 5–11.

    Google Scholar 

  • McCulloch, W. S. and Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematics and Biophysics, 5, 115–133.

    Google Scholar 

  • Montgomery, D. C. and Peck, E. A. (1982) Introduction to Linear Regression. John Wiley, NY.

    Google Scholar 

  • Rechenberg, I. (1973) Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzborg, Stuttgart.

    Google Scholar 

  • Rissanen, J. (1984) Universal coding, information, prediction and estimation. IEEE Transactions on Information Theory, 30, 629–635.

    Google Scholar 

  • Rosenblatt, F. (1958) The perceptron: a probabilisitic model for information storage and organization in the brain. Psychological Review, 65, 386.

    Google Scholar 

  • Schaffer, J. D. and Eshelman, L. J. (1991) On crossover as an evolutionarily viable strategy, Proc. of the Third International Conference on Genetic Algorithms, eds. R. K. Belew and L. B. Booker, pp. 61–68. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Schwefel, H.-P. (1965) Kybernetische Evolution als Strategie der Experimentellen Forschungin der Strömungstechnik, Diploma Thesis, Technical University of Berlin.

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

    Google Scholar 

  • Simon, H. A. and Newell, A. (1958) Heuristic problem solving: the next advance in operations research. Operations Research, 6, 6.

    Google Scholar 

  • Simpson, P. K. (1992) Fuzzy min-max neural networks—Part 2. Clustering, IEEE Transactions on Fuzzy Systems, 1, 32–45.

    Google Scholar 

  • Spears, W. M. (1992) Crossover or mutation? Foundations of Genetic Algorithms 2, ed. L. D. Whitley, pp. 221–237. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Whitley, D. (1994) A genetic algorithm tutorial. This issue.

  • Widrow, B. (1987) The original adaptive neural net broom-balancer. Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 351–357.

  • Wieland, A. P. (1990) Evolving controls for unstable systems. In Connectionist Models'. Proceedings of the 1990 Summer School, eds. D. S. Touretsky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton. pp. 91–102. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Zadeh, L. A. (1965) Fuzzy sets. Information and Control, 8, 338–353.

    Google Scholar 

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Fogel, D.B. Evolutionary programming: an introduction and some current directions. Stat Comput 4, 113–129 (1994). https://doi.org/10.1007/BF00175356

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