Explore MATLAB source code curated for "GA" with clean implementations, documentation, and examples.
MATLAB source code for a fundamental genetic algorithm (GA) framework! This implementation serves as a foundation for developing various enhanced genetic algorithms through modifications and extensions.
Implementing GA-based parameter optimization for LSSVM in MATLAB platform, featuring practical code examples and algorithm explanations.
MATLAB source code implementation for applying genetic algorithms (GA) to antenna array design optimization, including fitness evaluation, crossover, and mutation operations.
A comprehensive collection of genetic algorithm source codes that I have compiled and developed, featuring various GA implementations such as standard GA, Simple GA (SGA), Adaptive GA (AGA), Traveling Salesman Problem GA (TSPGA), PID controller optimization GA (GAPID), Non-dominated Sorting GA (NSGA), and NSGA-II with elite preservation strategy.
The PSO-GA hybrid optimization algorithm delivers superior optimization performance compared to standalone PSO or GA implementations, featuring enhanced global exploration and local exploitation capabilities.
Statistical algorithms featuring numerous classic multivariate statistical techniques such as PLS, PCR, GA, PCR-UVE, PLS-UVE, UVE-CV, PLS-GA, MLR, LWR, Artificial Neural Networks, SPA, LSS with code implementation insights
Genetic Algorithm PID Tuning Problem: Known system parameters K=1 and T=2, using GA to optimize PID parameters. Encoding can be binary or real-valued with flexible bit length. Population size (M), crossover rate (Pc), and mutation rate (Pm) are user-selectable. Performance metrics are evaluated over a simulation period with Q=100 calculation steps. The implementation involves designing fitness functions that quantify system performance and genetic operators for parameter space exploration.
A hybrid algorithm combining PSO and GA that performs crossover and mutation operations on poorly performing particles in the PSO framework.
GA (Simple Genetic Algorithm) is a powerful intelligent multivariable optimization algorithm that mimics population reproduction dynamics for optimization. This SGA implementation supports variable optimization for both minimization and maximization problems (converted to minimization via function reciprocal), featuring floating-point encoding, Gray encoding, and binary encoding; selection methods include roulette wheel selection and tournament selection; crossover operations support single-point, uniform, and arithmetic crossover; mutation operations include single-point and floating-point mutation.
Request for source code implementations of Job Shop Scheduling Problem (JSSP) using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), hybrid PSO (hPSO), and Cooperative PSO (CPSO) with algorithm-specific enhancements and technical explanations.