MATLAB Implementation of Adaptive Genetic Algorithm

Adaptive Genetic Algorithm (AGA) enhances search efficiency by dynamically adjusting crossover and mutation probabilities based on fitness values. This article explains key implementation aspects in MATLAB, focusing on how probabilities adapt according to fitness evolution. The core concept involves automatic regulation of crossover probability (Pc) and mutation probability (Pm) based on individual fitness: when an individual's fitness approaches the population optimum, Pc and Pm decrease to preserve superior genes; conversely, lower-fitness individuals receive higher Pm values to increase population diversity. Implementation typically follows these steps: Parameter Initialization: Define base Pc/Pm values, population size, and iteration count using MATLAB's variable declaration (e.g., popSize = 100; maxGen = 500). Fitness Evaluation: Compute fitness scores through vectorized operations (e.g., fitness = objectiveFunction(population)). Dynamic Probability Adjustment: Recalculate Pc/Pm using conditional formulas comparing individual fitness to population maximum/average. For example: high-performance individuals might have Pc reduced from 0.9 to 0.6, while low-performance individuals see Pm increase from 0.01 to 0.1. Genetic Operations: Execute selection (roulette/tournament), crossover (single-point/two-point), and mutation using adjusted probabilities with logical indexing. Termination Criteria: Stop when reaching maximum generations or meeting solution threshold (while/if-break structures). This approach balances global exploration (via high mutation rates) and local exploitation (via controlled crossover), particularly effective for multimodal optimization problems. MATLAB's matrix operations efficiently handle population data manipulations, while built-in plotting functions (plot, scatter) visually demonstrate convergence progress through generation-by-generation fitness plots.