Review of evolutionary computation

This paper provides a comprehensive elaboration of evolutionary computation (EC). It first explains that evolutionary computation is a global optimization algorithm inspired by biological evolution. Based on the principles of natural selection and genetics, it can find approximate globally optimal solutions without requiring functions to be continuous, differentiable, or unimodal. The process is similar to biological evolution, where candidate solutions are iteratively improved. The paper further details the principles, processes, characteristics, and applications of classic evolutionary algorithms, such as genetic algorithms (GAs), ant colony optimization (ACO) algorithms, particle swarm optimization (PSO) algorithms, differential evolution (DE) algorithms, estimation of distribution algorithms (EDAs), and immune algorithms (IAs). Finally, the paper discusses the latest research achievements and applications both domestically and internationally, showcasing the wide-ranging and continuous development of evolutionary computation across various fields. Research in this area continues to focus on enhancing algorithm performance, adaptability, and problem-solving capabilities. It encompasses both theoretical and practical aspects and holds significant importance for fields such as science and engineering.