Comprehensive Genetic Algorithms Implementation

This MATLAB program provides implementations of multiple genetic algorithms, offering comprehensive coverage and high computational efficiency for solving diverse optimization problems. The codebase includes fundamental genetic algorithm variations with modular implementations of key components: population initialization using random sampling or Latin hypercube methods, fitness evaluation functions supporting both minimization and maximization objectives, selection operators (roulette wheel, tournament selection), crossover operations (single-point, two-point, uniform crossover), and mutation mechanisms (bit-flip, Gaussian mutation). The implementation supports constraint handling through penalty functions or specialized operators and includes convergence monitoring with adaptive parameter tuning. Suitable for research, engineering projects, and educational purposes, this toolbox can optimize mathematical functions, solve scheduling problems, perform parameter calibration, and handle complex multi-objective scenarios. The object-oriented design allows easy extension for custom operators, while pre-configured examples demonstrate applications in combinatorial optimization, continuous parameter spaces, and mixed-integer problems. Both professionals and beginners can utilize this resource to achieve improved results in genetic algorithm applications.