# A Cellular Automata-Based Crossover Operator for Binary Chromosome Population Genetic Algorithms
###### Abstract  
In this paper, we propose a crossover operator for genetic algorithms with binary chromosomes populations based on the cellular automata (_CGACell_). 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 _CGACell_ 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 _CGACell_ 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.  
genetic algorithm (GA); cellular automata (CA); elementary cellular automata (ECA); crossover operators; K-nearest neighbors (KNN); Kmeans; face images classification; principal component analysis (PCA) Article  
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the optimization of problems by determining the optimal solutions. Genetic algorithms are optimization algorithms inspired by the process of natural selection through which representative individuals are advantaged, and it was developed by [PERSON] (1989) [4]. Applications using GA include data clustering and mining, neural networks, image processing, feature selection for machine learning, medical science, learning robot behavior, the traveling salesman problem, vehicle routing problems, financial markets, manufacturing systems, and mechanical engineering design [5,6].  
In the process of searching for solutions in the solution space, a genetic algorithm performs an evolutionary transformation of the population, over several generations, by using the genetic operators of selection, crossover, and mutation, and the quality of the descendants of the chromosomes in the current population is evaluated by the function fitness that decides the composition of the new population of chromosomes. An essential role in the evolutionary process of a genetic algorithm is represented by the reproduction stage, in which the chromosomes, selected to participate in the formation of the new generation, determine the new descendants through the gene crossing operation [7,8]. Thus, the crossover problem has many developments in specialized research, establishing experimental performances from classic methods (with one point, with two or more crossing points--uniform or ordered) to techniques to solve object classification problems (Looseness control crossover or Greedy partition crossover) [9, 10, 11, 12, 13].  
The aim of the paper is to propose a crossover operator for genetic algorithms using cellular automata and to establish the experimental performance in specific data classification applications.