Clonal selection algorithms: a comparative case study using effective mutation potentials

Authors:
Vincenzo Cutello;Giuseppe Narzisi;Giuseppe Nicosia;Mario Pavone
Affiliations:
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;Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Venue:
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Year:
2005

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Abstract

This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.