Genetic Algorithm Source Code Package for MATLAB Environment
A compressed package containing genetic algorithm source code implementation for MATLAB
Multiple Approaches for Solving the Traveling Salesman Problem (TSP)
This article explores various algorithms for TSP optimization including Genetic Algorithms, A* Algorithm, Dijkstra's Algorithm, Simulated Annealing, and Neural Networks, with code implementation insights
PID Controller Design Using Genetic Algorithm (GA)
A PID controller was developed using Genetic Algorithm (GA), demonstrating excellent performance in simulation results with detailed implementation insights.
MATLAB Implementation Program for Genetic Algorithms
Multiple examples and various computational methods for genetic algorithm implementation in MATLAB, featuring diverse optimization scenarios and algorithmic approaches
Genetic Algorithm for Reactive Power Optimization in Power Systems
MATLAB source code implementation of genetic algorithms for reactive power optimization in electrical power systems
Solving TSP Problem Using Genetic Algorithm
MATLAB source code implementing genetic algorithm for Traveling Salesman Problem (TSP) optimization with detailed parameter configuration and evolutionary operations.
Genetic Algorithm Toolbox Enhancements
Supplementing MATLAB's genetic algorithm toolbox with missing but commonly used crossover and mutation function files to extend functionality and flexibility
Image Fusion Quality Evaluation Function for Genetic Algorithm
MATLAB-based implementation of an evaluation function for assessing fused image quality, designed specifically for use with genetic algorithms in image fusion applications.
NSGA-II Multi-Objective Reactive Power Optimization Algorithm
NSGA-II multi-objective reactive power optimization algorithm implementing genetic algorithm, non-dominated sorting, and forward-backward sweep power flow calculation methods with population initialization, crossover, mutation operations and Pareto front solutions
Solving TDOA Problems in Communications Using Genetic Algorithms
Genetic Algorithm Implementation for TDOA Localization with Multiple Test Analysis - This project contains several core modules: program (main controller for multiple test iterations), definition_constant (parameter configuration), main_program (single trial execution), all_Noise (noise-corrupted TDOA calculation), and gen_ini_pop_arr (chromosome population initialization). The system performs account_test trials to find optimal chromosomes for each test while computing mean value (MV) and mean squared error (MSE) metrics.