Explore MATLAB source code curated for "量子遗传算法" with clean implementations, documentation, and examples.
Introducing quantum state vectors into genetic encoding and utilizing quantum logic gates for chromosome evolution to achieve superior performance compared to conventional genetic algorithms
Examination of crucial functions in quantum genetic algorithms, providing valuable insights for algorithm implementation and practical applications
This implementation utilizes quantum encoding for chromosome representation and employs genetic algorithm optimization techniques for function optimization, featuring quantum-bit encoding and genetic operators for solution space exploration.
MATLAB source code implementation for optimizing standard functions using quantum genetic algorithms
Implementation of adaptive image enhancement through parameter optimization using quantum genetic algorithms, with code-level descriptions of quantum bit representation and fitness evaluation mechanisms.
This algorithm implements an image enhancement approach based on quantum genetic algorithm, consisting of multiple MATLAB .m sub-files. The method utilizes quantum rotation gate operations to optimize image quality through evolutionary computation.
Quantum Genetic Algorithm (QGA) - A hybrid optimization approach merging quantum computing principles with evolutionary algorithms
Download the Quantum Genetic Algorithm Maximum Entropy Method Image Multi-Threshold Segmentation Program. Threshold quantity can be directly specified through parameter configuration.
Complete MATLAB source code for Quantum Genetic Algorithm including multiple modular files - simply run main.m for testing and demonstration
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.