Genetic Algorithm for Function Extremum Optimization
Basic Genetic Algorithm Implementation for Solving Function Extremum Problems
Genetic Algorithm, Quantum Genetic Algorithm, and Novel Genetic Algorithm: A Comparative Analysis
This program provides a comparative analysis of three evolutionary algorithms - Genetic Algorithm (GA), Quantum Genetic Algorithm (QGA), and a Novel Genetic Algorithm - using mathematical function optimization as test cases. The implementation demonstrates key relationships, differences, and performance characteristics between these algorithms through practical code examples and benchmark testing.
Parameter Identification for ARX Models Using Genetic Algorithm (GA)
GA-based parameter identification methodology for AutoRegressive with eXogenous input (ARX) models with evolutionary optimization implementation
Genetic Algorithm: Table of Contents and Various Example Cases
Comprehensive directory of genetic algorithms with multiple practical examples, featuring code implementation details and algorithm explanations to accelerate learning for beginners
Clustering Algorithm Based on Genetic Simulated Annealing Optimization
While traditional genetic algorithms exhibit significant individual diversity during early iterations, the classic roulette wheel selection mechanism causes offspring production to correlate directly with parental fitness values. This often leads to premature convergence as superior individuals dominate the population prematurely. During later stages, fitness values tend to converge, diminishing the reproductive advantages of elite individuals and stalling evolutionary progress. The algorithm incorporates fitness scaling where temperature-controlled annealing maintains balanced selection pressure during high-temperature phases (early iterations), while intensified scaling at lower temperatures amplifies fitness differences to accentuate elite advantages. This hybrid approach leverages complementary strengths of simulated annealing and genetic algorithms to overcome premature convergence, with customized genetic encoding and fitness functions specifically designed for clustering problems to ensure efficient global convergence.
Electric Power Distribution Network Reconfiguration
This program implements electric power distribution network reconfiguration using a MATLAB-based genetic algorithm for network topology optimization and load balancing
Real-Coded Genetic Algorithm with Practical Implementation Examples
Real-coded genetic algorithm featuring concrete implementation examples and excellent usability for continuous optimization problems.
Performance Testing of Intelligent Algorithms Using Particle Swarm Optimization, Genetic Algorithms, and Others
The Rosenbrock test function on the MATLAB platform is highly valuable for assessing the performance of intelligent algorithms such as Particle Swarm Optimization and Genetic Algorithms, enabling comprehensive evaluation of convergence behavior and search capabilities.
Enhancing BP Neural Network Models Using Genetic Algorithms
Improving BP neural network models with genetic algorithms provides effective solutions for optimization and prediction problems through evolutionary optimization techniques and neural network parameter tuning.
Function Optimization Using Genetic Algorithm
Optimizing the function f(x) = Σ(i=1 to n) x_i * sin(√|x_i|) using genetic algorithm to obtain convergence values, with visualization through 3D plots and fitness evolution curves.