Abstract
A number of authors made the claim that a multiobjective approach preserves genetic diversity better than a single objective approach. Sofar, none of these claims presented a thorough analysis to the effect of multiobjective approaches. In this paper, we provide such analysis and show that a multiobjective approach does preserve reproductive diversity. We make our case by comparing a pareto multiobjective approach against a single objective approach for solving single objective global optimization problems in the absence of mutation. We show that the fitness landscape is different in both cases and the multiobjective approach scales faster and produces better solutions than the single objective approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
H.A. Abbass. Self-adaptive pareto diffierential evolution. In The IEEE Congress on Evolutionary Computation, Honolulu, USA, pages 831–836. IEEE Press, 2002.
H.A. Abbass. A memetic pareto evolutionary approach to artificial neural networks. In M. Stumptner, D. Corbett, and M. Brooks, editors, Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI’01), pages 1–12, Berlin, 2001. Springer-Verlag.
H.A. Abbass. An evolutionary artificial neural network approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25(3):265–281, 2002.
S. Bleuler, M. Brack, L. Thiele, and E. Zitzler. Multiobjective genetic programming: Reducing bloat using SPEA2. In Proceedings of the 2001 Congress on Evolutionary Computation CEC 2001, pages 536–543. IEEE Press, 2001.
E. De Jong, R. Watson, and J. Pollack. Reducing bloat and promoting diversity using multi-objective methods. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 11–18, 2001.
K De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, 1975.
V.S. Gordon and D. Whitley. Serial and parallel genetic algorithms as function optimization. In The 5th International Conference on Genetic Algortihms, pages 177–183. Morgan Kaufmann, 1993.
J. Knowles and D. Corne. Reducing local optima in single-objective problems by multi-objectivization. In First International Conference on Evolutionary Multicriterion Optimization (EMO’01), pages 269–283. Springer-Verlag, 2001.
H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithms: continuous parameter optimization. Evolutionary Computation, 1(1):25–49, 1993.
H. Mühlenbein and D. Schlierkamp-Voosen. The science of breeding and its application to the breeder genetic algorithm bga. Evolutionary Computation, 1(4):335–360, 1994.
V.K. Vassilev, T.C. Fogarty, and J.F. Miller. Information characteristics and the structure of landscapes. Evolutionary Computation, 8(1):31–61, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abbass, H.A., Deb, K. (2003). Searching under Multi-evolutionary Pressures. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_28
Download citation
DOI: https://doi.org/10.1007/3-540-36970-8_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-01869-8
Online ISBN: 978-3-540-36970-7
eBook Packages: Springer Book Archive