Clonal Selection Algorithm with Dynamic Population Size for Bimodal Search Spaces | Zendy

Vincenzo Cutello | Zendy; Doheon Lee | Zendy; S. Leone | Zendy; Giuseppe Nicosia | Zendy; Mario Pavone | Zendy

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Clonal Selection Algorithm with Dynamic Population Size for Bimodal Search Spaces

Author(s) -

Vincenzo Cutello,

Doheon Lee,

S. Leone,

Giuseppe Nicosia,

Mario Pavone

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-45901-4

DOI - 10.1007/11881070_125

Subject(s) - maxima and minima , dimension (graph theory) , population , computer science , evolutionary algorithm , population size , convergence (economics) , selection (genetic algorithm) , constant (computer programming) , algorithm , evolutionary computation , trap (plumbing) , artificial immune system , cloning (programming) , mathematical optimization , artificial intelligence , mathematics , combinatorics , physics , mathematical analysis , demography , sociology , meteorology , economics , programming language , economic growth

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.

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