An Information-Theoretic Approach for Clonal Selection Algorithms

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.