Explore MATLAB source code curated for "遗传算法" with clean implementations, documentation, and examples.
A MATLAB program for extracting coupling coefficients of quasi-elliptic function cross-coupled filters, utilizing Genetic Algorithm and Solvopt optimization toolkit. The implementation includes iterative parameter tuning and convergence analysis. For usage guidance, contact QQ: 13319810.
Classic Genetic Algorithm with Practical Code Examples
This MATLAB source code implements a genetic algorithm incorporating elitism strategy. The implementation details the complete genetic algorithm workflow and significantly improves performance over basic genetic algorithms by preserving elite individuals across generations. The code includes key functions for selection, crossover, mutation, and elite preservation mechanisms.
Simulation program for cognitive radio cognitive engine utilizing genetic algorithm optimization with parameter tuning and performance evaluation capabilities
Combining Genetic Algorithm and Fuzzy C-Means Clustering for SAR image classification, implementing two-class or three-class segmentation with optimization techniques and fuzzy logic handling.
Hybrid programming combining genetic algorithms and neural networks for optimization computations, including implementation approaches and technical considerations. For reference purposes only.
Implementation of hybrid algorithm programs combining genetic algorithms with neural networks and particle swarm optimization with neural networks, enabling comparative analysis of algorithmic performance across different optimization and machine learning tasks.
Implementing stock selection analysis by applying Data Envelopment Analysis (DEA) and Genetic Algorithm (GA) to fundamental data of listed companies. The genetic algorithm optimizes the selection of fundamental indicators, while DEA evaluates stock efficiency scores through mathematical programming techniques.
A comprehensive set of practical and user-friendly heuristic optimization algorithms, including non-adaptive algorithms, simulated annealing-based population algorithms, basic genetic algorithms, differential evolution algorithms, and particle swarm optimization. Additionally features the Sacred Algorithm which integrates all these optimization operators with occasional algorithm swapping between different populations.
High-value practical implementation combining genetic algorithm optimization with backpropagation neural networks in MATLAB, featuring complete source code with data examples and documentation.