Genetic Algorithm MATLAB Implementation

This text introduces the classic genetic algorithm, which is a heuristic search technique for optimization problems that simulates the process of natural selection. The algorithm searches for optimal solutions from the current solution space through genetic operations such as crossover, mutation, and selection. In MATLAB implementations, key functions typically include population initialization (using rand or randi), fitness evaluation, selection operators (like tournament selection or roulette wheel selection), crossover operations (single-point or multi-point crossover with rand-based position selection), and mutation operations (bit-flip or Gaussian mutation). Genetic algorithms find applications in various fields such as engineering optimization and machine learning. Their widespread applicability stems from the ability to find global optimum solutions in complex problems, demonstrating strong robustness and adaptability. Even when dealing with large or uncertain solution spaces, genetic algorithms can achieve satisfactory results through proper parameter tuning (e.g., population size, mutation rate) and generations of evolution.