Genetic Algorithm for Function Extremum Optimization

Genetic Algorithm is a widely-used optimization algorithm employed for solving function extremum problems. Based on biological evolution principles, it searches for optimal solutions by simulating natural selection, crossover, and mutation processes. This algorithm finds extensive applications in engineering optimization, machine learning, and data mining. The basic genetic algorithm represents a simplified form consisting of three core operations: selection, crossover, and mutation. Through iterative optimization cycles, the algorithm converges toward optimal solutions while providing feasible implementation approaches. In practical implementation, key components include fitness function evaluation to measure solution quality, tournament selection for parent chromosome selection, single-point crossover for generating offspring, and random mutation operators to maintain population diversity. The algorithm typically initializes with a random population and evolves through generations using elitism preservation strategies to prevent losing the best solutions.