Genetic Algorithm for Integer Programming
Applying genetic algorithms to solve integer programming problems by constraining parameters to integers through GA function implementation for mixed-integer optimization.
Array Antenna Synthesis Using Genetic Algorithm
Array antenna synthesis based on genetic algorithms provides significant assistance to researchers studying array antennas, with implementation involving population initialization, fitness evaluation, selection, crossover, and mutation operations.
Path Planning for Mobile Robots Using Genetic Algorithms
Includes documentation, original program code suitable for graduation projects, and simulation of robot path planning with enhanced algorithmic implementation details.
Drone Path Planning Optimization
Drone path planning using Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)
Optimization Design of LQR Controller Based on Genetic Algorithm
Applying genetic algorithm to LQR controller design, leveraging its global search capability to optimize weighting matrices using active suspension performance metrics as objective functions, thereby improving LQR design efficiency and performance. Implementation involves chromosome encoding for matrix parameters, fitness evaluation based on system response, and iterative optimization through selection, crossover, and mutation operations.
GA-ELM: Genetic Algorithm Optimized Extreme Learning Machine with Data Implementation
Implementation of Extreme Learning Machine (ELM) optimized using Genetic Algorithm, complete with data availability and code integration
Solving Job Shop Scheduling Problem with Genetic Algorithm
This implementation solves the classic Job Shop Scheduling Problem (JSSP), featuring model explanations, Gantt chart generation methods, and practical code demonstrations. The program includes comprehensive documentation for scheduling beginners and requires execution through the main file with proper parameter configuration.
University of North Carolina Genetic Algorithm Optimization Toolbox
Univariate Function Optimization Backpropagation Neural Network Initial Weights and Thresholds Optimization
PID Control with Genetic Algorithm Optimization
PID control is widely used in industrial production, but obtaining optimal PID parameters remains challenging. This example provides a reference for PID control enthusiasts by demonstrating genetic algorithm-based PID optimization. The implementation includes parameter tuning through selection, crossover, and mutation operations to achieve better system performance.
MATLAB GADS Toolbox: Genetic Algorithm and Direct Search Toolbox
Application Background: Using MATLAB-based genetic algorithm toolboxes is highly convenient as they provide comprehensive function libraries for evolutionary computation. Popular toolboxes include GATBX and GAOT from the University of Sheffield, and MathWorks' GADS (Genetic Algorithm and Direct Search Toolbox), which is MATLAB's built-in optimization toolkit. Many users encounter function call issues due to toolbox differences, particularly when mixing GATBX functions with GADS environments. Key Technology: MATLAB's native genetic algorithm implementation through GADS differs from third-party toolboxes in function libraries and syntax, requiring specific path configuration and version compatibility checks for proper code execution.