Evolutionary computation

Evolutionary Computation is a computational method based on the principles of natural selection and genetics. It solves complex optimization and search problems by simulating the process of biological evolution. The core idea of evolutionary computation is to use the diversity of populations and the mechanism of natural selection to gradually approach the optimal solution.
The main branches of evolutionary computation include:
Genetic Algorithm (GA): Genetic algorithms find optimal solutions by simulating the processes of natural selection and genetic variation. They involve steps such as selection, crossover, mutation, and fitness evaluation. Genetic algorithms are widely used in optimization problems, machine learning, and artificial intelligence.
Evolution Strategy (ES): Evolution strategies are mainly used for continuous optimization problems, focusing on the adaptive variation of individuals. They improve the population through selection and mutation operations to ultimately find the optimal solution.
Evolutionary Programming (EP): Evolutionary programming emphasizes the process of individual variation and selection, typically used to solve complex optimization problems. Unlike genetic algorithms, evolutionary programming does not use crossover operations.
Genetic Programming (GP): Genetic programming is an extension of genetic algorithms used to automatically generate computer programs. It optimizes the structure and parameters of programs through an evolutionary process to solve specific problems.
Differential Evolution (DE): Differential evolution is an evolutionary algorithm used for global optimization, particularly suitable for continuous variable optimization problems. It finds the optimal solution through differential variation and selection among individuals.