Explore MATLAB source code curated for "遗传算法" with clean implementations, documentation, and examples.
Implementing a genetic algorithm to solve the knapsack problem, including population initialization, crossover operations, mutation strategies, and penalty functions, with detailed code implementation approaches for effective constraint handling.
Implementation of genetic algorithms for multi-variable, multi-modal function optimization and multi-objective problem optimization with practical working examples and executable code.
Implementing genetic algorithms in MATLAB without using the built-in toolbox functions, featuring manual coding of selection, crossover, and mutation operations
This program demonstrates a step-by-step implementation of genetic algorithms for solving Traveling Salesman Problems (TSP), featuring detailed code execution processes. Simulation results confirm the program's feasibility and effectiveness, accompanied by comprehensive result visualizations.
Implementation of genetic algorithm-optimized BP neural network featuring a three-layer network architecture and elite preservation strategy for enhanced convergence
This program implements a genetic algorithm solution for target firepower allocation, guaranteed to produce your desired optimal results with robust optimization capabilities.
Implementation of RBF network optimization using Particle Swarm Optimization and Genetic Algorithms, featuring comparative analysis with code-based methodology descriptions
Implementation and Solution of Projection Pursuit Grade Evaluation Model Using Genetic Algorithms
The combination of genetic algorithms and neural networks demonstrates excellent application in stock market forecasting, providing robust optimization techniques for predictive modeling.
Reactive power optimization with genetic algorithm implementation on IEEE 33-bus system case study