Genetic Algorithm for Optimal Band Selection
Combining Genetic Algorithm with Partial Least Squares Regression for spectral band optimization
Vehicle Routing Optimization in Logistics Distribution Using Genetic Algorithms
Genetic algorithms are recognized as one of the effective methods for solving NP-hard problems. When applied to vehicle routing optimization in logistics distribution, the traditional genetic algorithm is enhanced by incorporating principles from immune algorithms. Experimental results demonstrate that this hybrid approach exhibits superior global and local search capabilities, faster convergence rates, and effectively addresses vehicle routing optimization challenges in logistics distribution.
Function Optimization (With or Without Constraints) and Combinatorial Optimization Algorithms
This article provides an in-depth explanation of the principles and source code implementations for function optimization (supporting both constrained and unconstrained problems) and combinatorial optimization algorithms. The presented algorithms demonstrate exceptional computational efficiency and are suitable for practical applications. Additional genetic algorithm examples are included in the attachments for further research and algorithm study.
Genetic Algorithm-Partial Least Squares (GA-PLS) for Quantitative Analysis
Genetic Algorithm-Partial Least Squares method for quantitative analysis, combining evolutionary optimization with multivariate statistical modeling for enhanced data processing efficiency.
Genetic Algorithm for Vehicle Routing Problem (VRP) Optimization
Solving VRP with Genetic Algorithm: Implementation and Code Approaches
MATLAB Code Implementation for Solving Vehicle Routing Problem (VRP)
This program provides a MATLAB implementation using genetic algorithms to solve basic Vehicle Routing Problems. The distance matrix between supply points and genetic algorithm parameters are defined in VRP.M. The fitness function is calculated based on route length, while constraint handling is implemented by adding penalty factors in the LEN function - for simplicity, infeasible routes are penalized with an additional 100 units distance. The implementation demonstrates core GA components including chromosome encoding, selection, crossover, and mutation operations specific to VRP optimization.
MATLAB Intelligent Algorithm Implementation: 30 Case Studies
"MATLAB Intelligent Algorithm Implementation: 30 Case Studies" presents intelligent algorithms through practical case studies, focusing on MATLAB implementations of widely-used techniques including Genetic Algorithms, Immune Algorithms, Simulated Annealing, Particle Swarm Optimization, Artificial Fish Swarm Algorithm, Ant Colony Optimization, and Neural Network Algorithms. Each algorithm is accompanied by complete MATLAB code examples with practical implementation guidance, making this resource highly valuable for researchers and practitioners.
MATLAB Implementation of Genetic Algorithm with Code Optimization Features
A comprehensive MATLAB genetic algorithm program featuring modular sub-functions, adaptive multidimensional matrix parameter solving, floating-point real number optimization, and clearly structured code for easy understanding and implementation.
Short-Term Load Prediction Using Genetic Algorithm and Artificial Neural Network
This program implements short-term load forecasting through genetic algorithm optimization and artificial neural network modeling. The package contains detailed documentation that can be accessed by extracting the compressed archive, including code implementation specifics and algorithm configuration parameters.
Comprehensive Tutorial on Genetic Algorithms with MATLAB Implementation
Detailed introduction to genetic algorithms including core concepts and practical MATLAB implementation with code examples