Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation

Ling Wang

doi:10.1016/j.amc.2005.01.115

Return

Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation

2005-12-01

PRE

王玲 (Ling Wang)

Fang Tang

吴昊 (Hao Wu)

DOI：10.1016/j.amc.2005.01.115

Original

Original request for help

Save

Abstract

En 中文

Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named RQGA, is proposed by reasonably combining the Q-bit search of quantum algorithm in micro-space and classic genetic search of real-coded GA (RGA) in macro-space to achieve better optimization performances. Simulation results based on typical functions demonstrate the effectiveness of the hybridization, especially the superiority of RQGA in terms of optimization quality, efficiency as well as the robustness on parameters and initial conditions. Moreover, simulation results about model parameter estimation also demonstrate the effectiveness and efficiency of the RQGA. (c) 2005 Elsevier Inc. All rights reserved.

Keywords:

quantum computing

genetic algorithm

hybrid algorithm

numerical optimization

parameter estimation

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.