Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions

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Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions

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Symposium on Applied Computing archive
Proceedings of the 2006 ACM symposium on Applied computing table of contents

Dijon, France

SESSION: Evolutionary computation and optimization (ECO) table of contents

Pages: 945 - 949

Year of Publication: 2006

ISBN:1-59593-108-2

Authors

V. Cutello	University of Catania, V.le A. Doria
G. Nicosia	University of Catania, V.le A. Doria
P. S. Oliveto	University of Catania, V.le A. Doria

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary algorithm (EA) to optimize basic and complex trap functions. The designed evolutionary algorithm uses perturbation operators based on HyperMacromutation and stop at first constructive mutation heuristic. The experimental and theoretical results show that the algorithm successfully achieves its goal in facing this computational problem. The low number of evaluations to solutions expected through the theoretical analysis of the EA have been fully confirmed by the experimental results. To our knowledge the designed EA is the state-of-art algorithm to face trap function problems.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	V. Cutello, G. Narzisi, G. Nicosia, and M. Pavone. Clonal selection algorithms: A comparative case study using effective mutation potentials. In 4th International Conference on Artificial Immune Systems (ICARIS), pages 13--28, Banff, Alberta, Canada, Aug. 2005.
	2	A. Czarn, C. MacNish, K. Vijayan, B. Turlach, and R. Gupta. Statistical exploratory analysis of genetic algorithms. IEEE Trans. Evolutionary Computation, 8(4):405--421, 2004.
	3	K. Deb and D. E. Goldberg. Analyzing deception in trap functions. In L. D. Whitley, editor, FOGA, pages 93--108. Morgan Kaufmann, 1992.
	4	S. Nijssen and T. Bäck. An analysis of the behavior of simplified evolutionary algorithms on trap functions. IEEE Trans. on Evol. Comp., 7(1):11--22, 2003.
	5	A. Prügel-Bennett , A. Rogers, Modelling genetic algorithm dynamics, Theoretical aspects of evolutionary computing, Springer-Verlag, London, 2001