An Information-Theoretic Approach for Clonal Selection Algorithms View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Vincenzo Cutello , Giuseppe Nicosia , Mario Pavone , Giovanni Stracquadanio

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. More... »

PAGES

144-157

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-14547-6_12

DOI

http://dx.doi.org/10.1007/978-3-642-14547-6_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001799441


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