Robot Path Planning Using Genetic Algorithm
Grid-Based Genetic Algorithm for Robotic Path Planning
Multi-Objective Optimization Algorithm Based on Genetic Algorithms
Solving multi-objective optimization problems using genetic algorithms, including GUI implementation codes and optimization toolbox integration
Genetic Algorithm-Optimized BP Neural Network Algorithm
Optimizes neural network performance by using genetic algorithms to determine superior initial weights and thresholds, which enhances convergence and solution quality.
Road Image Segmentation Using Genetic Algorithm
Implementation of road image segmentation based on genetic algorithm, demonstrating effective performance in processing road images with detailed code-oriented methodology.
Genetic Algorithm-Optimized BP Neural Network Algorithm
Implementation Tutorial for Genetic Algorithm-Optimized BP Neural Network Algorithm For detailed explanations with code implementation examples, please refer to the included tutorial. Due to file size limitations, contact me for high-definition tutorials with complete MATLAB/Python code demonstrations.
Urban Traffic Signal Optimization Using an Enhanced Genetic Algorithm
Implementation of urban traffic signal optimization through an enhanced genetic algorithm, including complete MATLAB/Python code with detailed functions for crossover, mutation, and fitness evaluation
Workshop Layout Optimization Based on Genetic Algorithm with MATLAB Implementation
MATLAB source code for workshop layout optimization using genetic algorithms. Workshop layout optimization involves strategically arranging processing equipment, material handling systems, work units, and aisle corridors within a limited production space. This model represents a nonlinear continuous optimization problem with complex constraints that can be effectively solved using genetic algorithms. The code implementation includes chromosome encoding for layout configurations, fitness evaluation based on material flow efficiency, and genetic operators for solution evolution.
Genetic Algorithm-Based Multi-Objective Optimization Code Implementation
Code implementation for multi-objective optimization using genetic algorithms with detailed tutorial explanations. For high-resolution tutorials beyond file size limitations, please contact the author for additional materials.
Adaptive Resource Allocation Algorithm for OFDM Systems Based on Genetic Algorithm
MATLAB implementation of Genetic Algorithm-based adaptive resource allocation for OFDM systems. The OFDM adaptive resource allocation problem (including carrier and power allocation) represents a nonlinear optimization model containing both discrete and continuous decision variables with complex nonlinear constraints, making it suitable for intelligent optimization algorithms. The implementation utilizes genetic operations like selection, crossover, and mutation to evolve solutions toward optimal resource distribution.
Distribution Center Location Based on Genetic Algorithm
Source code and result data for Problem A (Supply Chain Network Establishment and Road Disruption) from the 2013 Northwestern Polytechnical University "Zhenghe Cup" Mathematical Modeling Competition. Algorithm: Genetic Algorithm-based Distribution Center Location with implementation details including chromosome encoding, fitness function design, and crossover/mutation operations.