MATLAB Implementation of Genetic Algorithm for Function Optimization

This MATLAB implementation demonstrates a genetic algorithm designed to find extreme values (minimum or maximum) of mathematical functions. The algorithm begins by defining a fitness function that quantifies each individual's performance in solving the optimization problem. In code terms, this typically involves creating a function that calculates the objective function value and potentially applies scaling or transformation. The selection operation employs techniques like roulette wheel selection or tournament selection to prioritize individuals with higher fitness scores for reproduction. This is commonly implemented using MATLAB's probability-based indexing or sorting functions. Crossover operations combine genetic material from two parent individuals to create offspring, often implemented through methods like single-point crossover or simulated binary crossover using array slicing and recombination techniques in MATLAB. Mutation introduces random changes to individual genes using approaches like Gaussian mutation or bit-flip mutation, typically implemented through random number generation and conditional modifications to maintain population diversity. Through iterative cycles of selection, crossover, and mutation operations - controlled by parameters like population size, generation count, and operator probabilities - the algorithm progressively improves population fitness toward finding optimal function values. The implementation includes convergence checks and best-solution tracking to terminate when optimal solutions are stabilized.