Optimizing BP Neural Networks Using Genetic Algorithms
Nonlinear Function Fitting with Hybrid Optimization Algorithm Implementation
Genetic Algorithm with Decimal Encoding
A MATLAB implementation of genetic algorithm using real-valued encoding for solving optimization problems.
Genetic Algorithm and BP Neural Network Control for Inverted Pendulum
A program for inverted pendulum control using genetic algorithm and BP neural network, implementing GA optimization of neural network weights and thresholds to achieve enhanced control performance
Genetic Algorithm-Enhanced Sparse Decomposition Algorithm
A refined sparse decomposition algorithm optimized through genetic algorithm integration, thoroughly debugged and implemented during academic paper development
Position Control of DC Motor Using Genetic Algorithm-Based PID Controller
Implementation of DC motor position control through genetic algorithm-optimized PID controller parameters, including proportional, integral, and derivative gain tuning strategies.
Genetic Algorithm Optimization of BP Neural Networks
This example demonstrates nonlinear function fitting by applying optimal individuals obtained from genetic algorithms to BP neural networks. The implementation involves MATLAB programming for genetic algorithm optimization of BP neural network models, utilizing key functions like ga() for evolutionary optimization and train() for network training.
Genetic Algorithm for Multi-Objective Optimization Problems
This implementation utilizes genetic algorithms to solve multi-objective optimization problems, featuring efficient code structure and practical solution approaches that provide valuable insights for researchers and developers.
Solving Reactive Power Optimization Problem Using Genetic Algorithm
MATLAB source code implementing genetic algorithm for reactive power optimization in power systems
MATLAB Genetic Algorithm for Single-Objective Optimization of Binary Function Maximization
MATLAB implementation of genetic algorithm for solving single-objective optimization problems to find maximum values of binary functions, featuring population initialization, fitness evaluation, selection, crossover, and mutation operations.
Genetic Algorithm MATLAB Toolbox Components
This code package contains partial implementations from the Genetic Algorithm MATLAB Toolbox, designed to work complementarily with GAmat1.rar and GAmat2.rar archives for comprehensive GA development.