Comparison Study for Clonal Selection Algorithm and Genetic Algorithm

TL;DR

This paper compares the performance of Clonal Selection Algorithm and Genetic Algorithm on benchmark functions, revealing that their effectiveness varies depending on the function type.

Contribution

It provides a comparative analysis of AIS-based Clonal Selection Algorithm and Genetic Algorithms, highlighting their relative strengths on different problem types.

Findings

Performance varies with function type

Clonal Selection outperforms on some functions

Genetic Algorithm outperforms on others

Abstract

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.