Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm

Autor:	Patel, Rahila, Shrawankar, Urmila, Raghuwanshi, MM., Jaiswal, Anil N.
Rok vydání:	2013
Předmět:	Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	This paper proposes a new scheme for performance enhancement of distributed genetic algorithm (DGA). Initial population is divided in two classes i.e. female and male. Simple distance based clustering is used for cluster formation around females. For reclustering self-adaptive K-means is used, which produces well distributed and well separated clusters. The self-adaptive K-means used for reclustering automatically locates initial position of centroids and number of clusters. Four plans of co-evolution are applied on these clusters independently. Clusters evolve separately. Merging of clusters takes place depending on their performance. For experimentation unimodal and multimodal test functions have been used. Test result show that the new scheme of distribution of population has given better performance. Comment: Pages: 09 Figures: 08 Tables: 05, International Journal of Digital Technology, 2009 pp 52-60
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1305.2830 Zobrazit plný text záznamu View this record from Arxiv