Several Quantum Algorithm Implementations
Source code implementations of quantum algorithms including Genetic Algorithm and Neural Network with computational optimizations
MATLAB Genetic Algorithm Implementation: Flight Scheduling Optimization Routine
Genetic Algorithm Implementation: MATLAB-based flight scheduling optimization using genetic algorithms for sequencing and conflict resolution, featuring chromosome encoding, fitness evaluation, and selection/crossover operations.
Genetic Algorithm for Optimization Problems
Implementation of genetic algorithms for optimization challenges including distribution network reconfiguration and fault recovery, featuring code-based solutions and algorithmic approaches.
MATLAB Implementation of Optimization Toolboxes
Optimization Toolbox, Particle Swarm Optimization Toolbox, University of North Carolina Genetic Algorithm Toolbox, University of Sheffield Genetic Algorithm Toolbox - with implementation approaches and key functions
Several Programs for Function Optimization
A collection of MATLAB programs implementing various function optimization techniques including Simulated Annealing, Tabu Search, Genetic Algorithms, and Neural Networks
Logistics Distribution Path Optimization Using Simulated Annealing and Genetic Algorithms
Implementation and Analysis of Logistics Distribution Route Planning with Simulated Annealing and Genetic Algorithm Hybrid Approach
Genetic Algorithm-based Route Planning and Path Planning Algorithms
Complete source code for genetic algorithm implementations in route planning and path planning applications, featuring comprehensive algorithm explanations and key function descriptions for collaborative learning
Genetic Algorithm Optimization of BP Neural Networks
Genetic Algorithms (GAs), proposed in 1962 by Professor Holland at the University of Michigan, are a parallel stochastic search optimization method that simulates natural genetic mechanisms and biological evolution. This approach introduces the biological evolution principle of "survival of the fittest" into encoded parameter populations, where individuals are selected based on fitness functions through genetic operations including selection, crossover, and mutation. High-fitness individuals are preserved while low-fitness individuals are eliminated, creating new populations that inherit previous generation information while demonstrating superior performance. The algorithm iterates until convergence criteria are met, typically involving population initialization, fitness evaluation, and genetic operator application in computational implementations.
Implementation of Genetic Algorithm for Job Shop Scheduling in MATLAB
MATLAB source code implementation of genetic algorithm for job shop scheduling with reference basic approach and practical programming insights
Genetic Algorithm Path Planning Simulation
Genetic Algorithm Path Planning Simulation, implementing robot path planning using genetic algorithm methodology, classified as static path planning with population evolution and fitness evaluation components