An improved gradient-based niching genetic algorithm and its application

Autor:	Nie Ru, Jia Yuge
Rok vydání:	2011
Předmět:	Mathematical optimization Start point Estimation theory Computer science Population-based incremental learning Convergence (routing) Genetic algorithm Point (geometry) Algorithm Selection (genetic algorithm) Premature convergence
Zdroj:	2011 IEEE International Conference on Computer Science and Automation Engineering.
DOI:	10.1109/csae.2011.5953225
Popis:	A new gradient-based niching genetic algorithm is proposed in this paper to solve the problems of premature convergence and slower searching ability near a point in conventional genetic algorithm. The best point of genetic algorithm is selected as the start point of gradient to improve the ability of finding the local best and restricted competition selection niche algorithm is used to ensure the population diversity. At last the proposed method is applied to estimate the parameters of nonlinear system. Results show that the proposed algorithm can not only improve the ability of finding the local best but also solves the problems of premature convergence.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::297b9199328b3f75a372c2c334cf8ab9 https://doi.org/10.1109/csae.2011.5953225 Zobrazit plný text záznamu