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Evolutionary Computation and Heuristics

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Meta-Heuristics

Abstract

Evolutionary computation techniques constitute an important category of heuristic search. Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; these heuristic rules provide guidelines for evaluating unfeasible and feasible individuals. This paper surveys such heuristics for discrete and continuous domains and discusses their merits and drawbacks.

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References

  • T. Back, F. Hoffmeister and H.-P. Schwefel, A Survey of Evolution Strategies. Proceedings of the Fourth International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1991) 2–9

    Google Scholar 

  • J.C. Bean and A.B. Hadj-Alouane, A Dual Genetic Algorithm for Bounded Integer Programs. Department of Industrial and Operations Engineering, The University of Michigan, TR 92–53 (1992).

    Google Scholar 

  • L. Davis, Genetic Algorithms and Simulated Annealing, (Morgan Kaufmann Publishers, Los Altos, CA, 1987).

    Google Scholar 

  • L. Davis, Handbook of Genetic Algorithms, (Van Nostrand Reinhold, New York, 1991).

    Google Scholar 

  • K.A. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral dissertation, University of Michigan, Dissertation Abstract International, 36(10), 5140B.

    Google Scholar 

  • L.J. Eshelman and J.D. Schaffer, Real-Coded Genetic Algorithms and Interval Schemata. In Foundations of Genetic Algorithms — 2, ed. D. Whitley, (Morgan Kaufmann, Los Altos, CA, 1993) 187–202.

    Google Scholar 

  • D.B. Fogel and L.C. Stayton, On the Effectiveness of Crossover in Simulated Evolutionary Optimization. BioSystems 32 (1994) 171–182.

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • A.B. Hadj-Alouane and J.C. Bean. A Genetic Algorithm for the Multiple-Choice Integer Program. Department of Industrial and Operations Engineering, The University of Michigan, TR 92–50 (1992).

    Google Scholar 

  • W. Hock and K. Schittkowski, Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, Vol.187, (Springer-Verlag, New York, 1987).

    Google Scholar 

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

    Google Scholar 

  • A. Homaifar, S. H.-Y. Lai and X. Qi, Constrained Optimization via Genetic Algorithms. Simulation 62 (1994) 242–254.

    Article  Google Scholar 

  • J.A. Joines and C.R. Houck, On the Use of Non-Stationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems With GAs. In Proceedings of the Evolutionary Computation Conference—Poster Sessions, part of the IEEE World Congress on Computational Intelligence, Orlando, 26–29 June 1994, 579–584.

    Google Scholar 

  • J.R. Koza, Genetic Programming, (MIT Press, Cambridge, MA, 1992).

    Google Scholar 

  • R. Le Riche, C. Vayssade, and R.T. Haftka, A Segragated Genetic Algorithm for Constrained Optimization in Structural Mechanics. Technical Report, Université de Technologie de Compiegne, France (1995).

    Google Scholar 

  • Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, (Springer-Verlag, 2nd edition, New York, 1994).

    Google Scholar 

  • Z. Michalewicz and N. Attia, In Evolutionary Optimization of Constrained Problems. Proceedings of the 3rd Annual Conference on Evolutionary Programming, eds. A.V. Sebald and L.J. Fogel, (World Scientific Publishing, River Edge, NJ, 1994) 97–108.

    Google Scholar 

  • Z. Michalewicz and C. Janikow, Handling Constraints in Genetic Algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1991) 151–157.

    Google Scholar 

  • Z. Michalewicz, T.D. Logan and S. Swaminathan, Evolutionary Operators for Continuous Convex Parameter Spaces. In Proceedings of the 3rd Annual Conference on Evolutionary Programming, eds. A.V. Sebald and L.J. Fogel, (World Scientific Publishing, River Edge, NJ, 1994) 84–97.

    Google Scholar 

  • Z. Michalewicz and J. Xiao, Evaluation of Paths in Evolutionary Planner/Navigator, In Proceedings of the 1995 International Workshop on Biologically Inspired Evolutionary Systems, Tokyo, Japan, May 30–31,1995, 45–52.

    Google Scholar 

  • D. Orvosh and L. Davis, Shall We Repair? Genetic Algorithms, Combinatorial Optimization, and Feasibility Constraints. In Proceedings of the Fifth International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1993) 650.

    Google Scholar 

  • J. Paredis, Co-evolutionary Constraint Satisfaction. In Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, (Springer-Verlag, New York, 1994) 46–55,

    Google Scholar 

  • D. Powell and M.M. Skolnick, Using Genetic Algorithms in Engineering Design Optimization with Non-linear Constraints. In Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 1993) 424–430.

    Google Scholar 

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

    Google Scholar 

  • R.G. Reynolds, An Introduction to Cultural Algorithms. In Proceedings of the Third Annual Conference on Evolutionary Programming, (World Scientific, River Edge, NJ, 1994) 131–139.

    Google Scholar 

  • R.G. Reynolds, Z. Michalewicz and M. Cavaretta, Using Cultural Algorithms for Constraint Handling in Genocop. Proceedings of the 4th Annual Conference on Evolutionary Programming, San Diego, CA, March 1–3, 1995.

    Google Scholar 

  • J.T. Richardson, M.R. Palmer, G. Liepins and M. Hilliard, Some Guidelines for Genetic Algorithms with Penalty Functions. In Proceedings of the Third International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1989) 191–197.

    Google Scholar 

  • J.D. Schaffer, Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. Doctoral dissertation, Vanderbilt University (1984).

    Google Scholar 

  • M. Schoenauer and S. Xanthakis, Constrained GA Optimization. In Proceedings of the Fifth International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1993) 573–580.

    Google Scholar 

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

    Google Scholar 

  • W. Siedlecki and J. Sklanski, Constrained Genetic Optimization via Dynamic Reward-Penalty Balancing and Its Use in Pattern Recognition. In Proceedings of the Third International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1989) 141–150.

    Google Scholar 

  • A. Smith and D.M. Tate, Genetic Optimization Using a Penalty Function. In Proceedings of the Fifth International Conference on Genetic Algorithms, (Morgan Kaufmann Publishers, Los Altos, CA, 1989) 499–505.

    Google Scholar 

  • N. Srinivas and K. Deb, Multiobjective Optimization Using Nondomi-nated Sorting in Genetic Algorithms. Department of Mechanical Engineering, Indian Institute of Technology, Kanput, India (1993).

    Google Scholar 

  • D. Whitley, V.S. Gordon, and K. Mathias, Lamarckian Evolution, the Baldwin Effect and function Optimization. In Proceedings of the Parallel Problem Solving from Nature, 3, (Springer-Verlag, New York, 1994), 6–15.

    Google Scholar 

  • A.H. Wright, Genetic Algorithms for Real Parameter Optimization. In Foundations of Genetic Algorithms, ed. G. Rawlins, First Workshop on the Foundations of Genetic Algorithms and Classifier Systems, Morgan Kaufmann Publishers, Los Altos, CA, 1991) 205–218.

    Google Scholar 

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© 1996 Kluwer Academic Publishers

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Michalewicz, Z. (1996). Evolutionary Computation and Heuristics. In: Osman, I.H., Kelly, J.P. (eds) Meta-Heuristics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1361-8_3

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  • DOI: https://doi.org/10.1007/978-1-4613-1361-8_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8587-8

  • Online ISBN: 978-1-4613-1361-8

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