AIRCC's International Journal of Computer Science and Information Technology

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Two metaheuristic algorithms namely Artificial Immune Systems (AIS) and Genetic Algorithms are classified as computational systems inspired by theoretical immunology and genetics mechanisms. In this work we examine the comparative performances of two algorithms. A special selection algorithm, Clonal Selection Algorithm (CLONALG), which is a subset of Artificial Immune Systems, and Genetic Algorithms are tested with certain benchmark functions. It is shown that depending on type of a function Clonal Selection Algorithm and Genetic Algorithm have better performance over each other.

Keywords

Metaheuristic Algorithms, Artificial Immune Systems, Clonal Selection Algorithm, Genetic Algorithms, Numerical Optimization.
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