Multi-Mutation Site Adaptive Genetic Algorithm MATLAB Source Code

Genetic Algorithm is a widely applied evolutionary computation technique. This resource provides fundamental principles and implementation approaches for the following genetic algorithm variants: 1. Basic Genetic Algorithm - featuring standard selection, crossover, and mutation operations with roulette wheel selection and single-point crossover implementations. 2. Sequential Selection Genetic Algorithm - implements ordered selection mechanisms that process chromosomes in sequence rather than random selection. 3. Fitness Scaling Genetic Algorithm - includes dynamic fitness function normalization techniques to maintain selection pressure throughout evolution. 4. Large Mutation Genetic Algorithm - employs enhanced mutation operators with larger mutation rates and ranges to increase population diversity. 5. Adaptive Genetic Algorithm - features self-adjusting parameters where crossover and mutation rates adapt based on population fitness statistics. 6. Two-Point Crossover Genetic Algorithm - implements dual crossover points for chromosome recombination, providing more complex gene exchange patterns. 7. Multi-Mutation Site Adaptive Genetic Algorithm - combines adaptive parameter control with multiple mutation locations, allowing dynamic adjustment of mutation sites and rates during evolution. Additionally, well-documented MATLAB source code is provided for practical implementation and study, including main algorithm frameworks, objective functions, and parameter configuration examples.