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
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).
L. Davis, Genetic Algorithms and Simulated Annealing, (Morgan Kaufmann Publishers, Los Altos, CA, 1987).
L. Davis, Handbook of Genetic Algorithms, (Van Nostrand Reinhold, New York, 1991).
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.
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.
D.B. Fogel and L.C. Stayton, On the Effectiveness of Crossover in Simulated Evolutionary Optimization. BioSystems 32 (1994) 171–182.
L.J. Fogel, A.J. Owens and M.J. Walsh, Artificial Intelligence through Simulated Evolution, (Wiley, New York, 1966).
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, (Addison Wesley, Reading, MA, 1989).
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).
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).
J.H. Holland, Adaptation in Natural and Artificial Systems, (University of Michigan Press, Ann Arbor, 1975).
A. Homaifar, S. H.-Y. Lai and X. Qi, Constrained Optimization via Genetic Algorithms. Simulation 62 (1994) 242–254.
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.
J.R. Koza, Genetic Programming, (MIT Press, Cambridge, MA, 1992).
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).
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, (Springer-Verlag, 2nd edition, New York, 1994).
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.
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.
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.
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.
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.
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,
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.
I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, (Frommann-Holzboog Verlag, Stuttgart, 1973).
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.
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.
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.
J.D. Schaffer, Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. Doctoral dissertation, Vanderbilt University (1984).
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.
H.-P. Schwefel, Numerical Optimization for Computer Models. (Wiley, Chichester, UK, 1981).
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.
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.
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).
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.
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.
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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
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