Clonal selection algorithm with dynamic population size for bimodal search spaces

Authors:
V. Cutello;D. Lee;S. Leone;G. Nicosia;M. Pavone
Affiliations:
Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Department of BioSystems, KAIST, IBM-KAIST Bio-Computing Research Center, Daejeon, Republic of Korea;Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 6
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Abstract

In this article an Immune Algorithm (IA) with dynamic population size is presented. Unlike previous IAs and Evolutionary Algorithms (EAs), in which the population dimension is constant during the evolutionary process, the population size is computed adaptively according to a cloning threshold. This not only enhances convergence speed but also gives more chance to escape from local minima. Extensive simulations are performed on trap functions and their performances are compared both quantitatively and statistically with other immune and evolutionary optmization methods.