A Cellular Automata-Based Crossover Operator for Binary Chromosome Population Genetic Algorithms - Doru Constantin; Costel Bălcău (Applied Sciences)
In this paper, we propose a crossover operator for genetic algorithms with binary chromosomes populations based on the cellular automata (<semantics>CGACell</semantics>). After presenting the fundamental elements regarding cellular automata with specific examples for one- and two- dimensional cases, the the most widely used crossover operators in applications with genetic algorithms are described, and the crossover operator based on cellular automata is defined. Specific forms of the crossover operator based on the ECA and 2D CA cases are described and exemplified. The <semantics>CGACell</semantics> crossover operator is used in the genetic structure to improved the KNN algorithm in terms of the parameter represented by the number of nearest neighbors selected by the data classification method. Validity and practical performance testing are performed on image data classification problems by optimizing the nearest-neighbors-based algorithm. The experimental study on the proposed crossover operator, by comparing a GA algorithm based on <semantics>CGACell</semantics> with GA algorithms based on other crossover methods, including classical GAs and permutation-based, heuristic, and hybrid methods, attests to good qualitative performance in terms of correctness percentages in the recognition of new images, as well as in classification and recognition applications of facial image classes corresponding to several persons.