MATLAB Implementation of Traditional Genetic Algorithm

The traditional genetic algorithm program typically consists of selection, crossover, and mutation operations. First, the selection operation employs fitness-based selection mechanisms (such as roulette wheel selection or tournament selection) to choose individuals from the population for reproduction. In MATLAB implementation, this often involves calculating fitness probabilities using functions like fitnesstheta = fitness/sum(fitness) and applying cumulative probability distributions for parent selection. Next, the crossover operation performs genetic exchange between selected individuals through techniques like single-point or multi-point crossover. A common MATLAB approach involves generating crossover points using crossover_point = randi([1, chromosome_length-1]) and swapping genetic segments between parent chromosomes to create new offspring combinations. Finally, the mutation operation introduces small random changes to individual genes using mutation probability parameters. In code implementation, this typically involves iterating through each gene with statements like if rand() < mutation_rate and flipping bits or modifying values to maintain population diversity. Through the iterative combination of these operations, the traditional genetic algorithm effectively simulates natural selection and genetic mutation processes, gradually optimizing population fitness toward optimal solutions.