article

A species conserving genetic algorithm for multimodal function optimization

Authors:

Marton E. Balazs,

Geoffrey T. Parks,

P. John ClarksonAuthors Info & Claims

Evolutionary Computation, Volume 10, Issue 3

Pages 207 - 234

https://doi.org/10.1162/106365602760234081

Published: 01 September 2002 Publication History

Abstract

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

References

[1]

Ackley, D. H. (1987). An empirical study of bit vector function optimization. In Davis, L., editor, Genetic Algorithms and Simulated Annealing, pages 170-204, Pitman, London, UK.

[2]

Beasley, D., Bull, D. R., and Martin, R. R. (1993). A Sequential Niche Technique for Multimodal Function Optimization. Evolutionary Computation, 1(2):101-125.

Digital Library

[3]

Cavicchio, D. J. (1970). Adaptive Search Using Simulated Evolution. Ph.D. thesis, University of Michigan, Ann Arbor, Michigan.

[4]

Cohoon, J. P. et al. (1987). Punctuated equilibria: a parallel genetic algorithm. In Grefenstette, J. J., editor, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pages 148-154, Lawrence Earlbaum, Hillsdale, New Jersey.

Digital Library

[5]

Cohoon, J. P. et al. (1991). Distributed Genetic Algorithms for the Floorplan Design Problem. IEEE Transactions on Computer-Aided Design, 10(4):483-492.

[6]

Davidor, Y. (1991). A naturally occurring niche and species phenomenon: the model and first results. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 257-263, Morgan Kaufmann, San Mateo, California.

[7]

Deb, K. (1989). Genetic Algorithms in Multimodal Function Optimization. Master's thesis, University of Alabama, Tuscaloosa, Alabama.

[8]

Deb, K. and Goldberg, D. E. (1989). An investigation of niche and species formation in genetic function optimization. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 42-50, Morgan Kaufmann, San Mateo, California.

Digital Library

[9]

Deb, K. and Goldberg, D. E. (1991). Analyzing deception in trap functions. IlliGAL Technical Report 91009, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Illinois.

[10]

De Jong, K. A. (1975). An Analysis of Behavior of a Class of Genetic Adaptive Systems. Ph.D. thesis, University of Michigan, Ann Arbor, Michigan.

Digital Library

[11]

Eby, D. et al. (1999). The optimization of flywheels using an injection island genetic algorithm. In Bentley, P. J., editor, Evolutionary Design by Computers, pages 167-190, Morgan Kaufmann, San Francisco, California.

[12]

Eshelman, L. J. (1991). The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In Rawlins, G. J. E., editor, Foundations of Genetic Algorithms, pages 265-283, Morgan Kaufmann, San Mateo, California.

[13]

Gen, M. and Cheng, R. (1997). Genetic Algorithms and Engineering Design. John Wiley and Sons, New York, New York.

Digital Library

[14]

Glover, F. (1989). Tabu Search - Part I. ORSA Journal on Computing, 1(3):190-206.

[15]

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

Digital Library

[16]

Goldberg, D. E. (1990). A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. Complex Systems, 4(4):445-460.

[17]

Goldberg, D. E. (1992). Construction of High-order Deceptive Functions using Low-order Walsh Coefficients. Annals of Mathematics and Artificial Intelligence, 5:35-48.

[18]

Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Grefenstette, J. J., editor, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pages 41-49, Lawrence Earlbaum, Hillsdale, New Jersey.

Digital Library

[19]

Gordon, V. S., Whitley, D., and Böhn, A. (1992). Dataflow parallelism in genetic algorithms. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature 2, pages 533-542, Elsevier Science, Amsterdam, The Netherlands.

[20]

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

Digital Library

[21]

Hughes, E. J. and Leyland, M. (2000). Using Multiple Genetic Algorithms to Generate Radar Point-scatterer Models. IEEE Transactions on Evolutionary Computation, 4(2):147-163.

Digital Library

[22]

Lienig, J. and Thulasiraman, K. (1993). A Genetic Algorithm for Channel Routing in VLSI Circuits. Evolutionary Computation, 1(4):293-311.

Digital Library

[23]

Mahfoud, S. W. (1995). Niching methods for genetic algorithms. IlliGAL Technical Report 95001, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Illinois.

Digital Library

[24]

Martin, W. N., Lienig, J., and Cohoon, J. P. (1997). Island (migration) models: evolutionary algorithms based on punctuated equilibria. In Bäck, T., Fogel, D. B., and Michalewicz, Z., editors, Handbook of Evolutionary Computation, pages C6.3:1-C6.3:16, Institute of Physics Publishing, Bristol, UK.

[25]

Mengshoel, O. J. and Goldberg, D. E. (1999). Probability crowding: deterministic crowding with probabilistic replacement. In Banzhaf, W., Daida, J., and Eiben, A. E., editors, Proceedings of the Genetic and Evolutionary Computation Conference 1999, pages 409-416, Morgan Kaufmann, San Francisco, California.

[26]

Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York, New York.

Digital Library

[27]

Parmee, I. C. (1999). A Review of Evolutionary/Adaptive Search in Engineering Design. Evolutionary Optimization, 1(1):13-39.

[28]

Sarma, J. and De Jong, K. (1997). Generation gap methods. In Bäck, T., Fogel, D. B., and Michalewicz, Z., editors, Handbook of Evolutionary Computation, pages C2.7:1-C2.7:5, Institute of Physics Publishing, Bristol, UK.

[29]

Spears, W. M. (1994). Simple subpopulation schemes. In Sebald, A. V. and Fogel, L. J., editors, Proceedings of the Third Annual Conference on Evolutionary Programming, pages 296-307, World Scientific, Singapore.

[30]

Storn, R. and Price, K. (1997). Differential Evolution a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4):341-359.

Digital Library

[31]

Whitley, D. (1989). The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 116-121, Morgan Kaufmann, San Mateo, California.

Digital Library

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A species conserving genetic algorithm for multimodal function optimization

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cover image Evolutionary Computation

Evolutionary Computation Volume 10, Issue 3

Fall 2002

108 pages

ISSN:1063-6560

EISSN:1530-9304

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Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 September 2002

Published in EVOL Volume 10, Issue 3

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