Explore MATLAB source code curated for "遗传算法" with clean implementations, documentation, and examples.
This algorithm addresses the missile target assignment problem through genetic algorithm optimization, which satisfies fundamental assignment principles while maximizing overall operational effectiveness. The implementation includes chromosome encoding for assignment solutions, fitness evaluation based on mission objectives, and evolutionary operators for solution refinement.
Modeling nonlinear systems using 1500 datasets for network training and 500 datasets for testing. Since BP neural networks typically initialize weights and thresholds randomly, they often get trapped in local minima. This method employs genetic algorithm optimization for initial weights and thresholds, with comparative analysis of pre- and post-optimization performance. Implementation includes population initialization, fitness function design based on MSE, and chromosome encoding of network parameters.
This implementation features code integrating Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for enhanced Support Vector Machine (SVM) performance, providing an excellent machine learning solution with comprehensive optimization capabilities.
This MATLAB program provides extensive implementations of various genetic algorithms, featuring comprehensive coverage and high efficiency for optimization problem-solving.
MATLAB implementation of single-threshold image segmentation using genetic algorithm optimization, featuring efficient population initialization, fitness evaluation, and threshold selection mechanisms.
Implementation of genetic algorithm for PID controller parameter optimization using MATLAB programming, applied to first-order plus time delay system control with step response simulation analysis.
A program integrating Genetic Algorithm with Partial Least Squares (PLS) for variable screening, designed to solve combinatorial optimization problems with excessive variables through intelligent feature selection and optimization techniques.
Implementation of multi-population chain-agent genetic algorithm including optimized code and experimental benchmark results. This research-oriented implementation features parallel population evolution with inter-agent communication chains. Please provide feedback if utilized. Contact me for related academic papers on this optimization methodology.
A MATLAB-based program implementing genetic algorithm for effective image segmentation with enhanced optimization capabilities
Solving the Traveling Salesman Problem (TSP) using genetic algorithms addresses one of the most famous challenges in mathematical optimization. The problem involves a salesman who must visit n cities exactly once and return to the starting city, aiming to minimize the total travel distance. This implementation utilizes evolutionary computation principles, including chromosome encoding of city sequences, fitness evaluation based on path length, and genetic operators like crossover and mutation to iteratively improve solutions.