Cutello, Vincenzo and Nicosia, Giuseppe and Pavone, Mario and Stracquadanio, Giovanni (2010) An Information-Theoretic Approach for Clonal Selection Algorithms. In: UNSPECIFIED, ? - ?.
Cutello, Vincenzo and Nicosia, Giuseppe and Pavone, Mario and Stracquadanio, Giovanni (2010) An Information-Theoretic Approach for Clonal Selection Algorithms. In: UNSPECIFIED, ? - ?.
Cutello, Vincenzo and Nicosia, Giuseppe and Pavone, Mario and Stracquadanio, Giovanni (2010) An Information-Theoretic Approach for Clonal Selection Algorithms. In: UNSPECIFIED, ? - ?.
Abstract
In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions; the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Rényi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values. © 2010 Springer-Verlag Berlin Heidelberg.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Uncontrolled Keywords: | Clonal selection algorithms; population-based algorithms; information theory; relative entropy; global numerical optimization |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 13 Feb 2017 13:59 |
Last Modified: | 05 Dec 2024 21:39 |
URI: | http://repository.essex.ac.uk/id/eprint/18710 |