LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions. Permissions & Reprints Download PDF (360.5 KB) Look Inside Book Chapter Tracking Extrema in Dynamic Environments Using a Learning Automata-Based Immune Algorithm Alireza Rezvanian Book Chapter CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems Vahid Noroozi Book Chapter Learning Automata Based Algorithms for Mapping of a Class of Independent Tasks over Highly Heterogeneous Grids S. Ghanbari Book Chapter A New Classifier Based on Attribute Weighted Artificial Immune System springer.com springerprotocols.com English GO Iran MSRT – e-journals HOME SHOPPING CART MY SPRINGERLINK BROWSE TOOLS HELP LOG IN SEARCH FOR AUTHOR OR EDITOR PUBLICATION VOLUME ISSUE PAGE GO Advanced Search Search Tips Page 1 of 2 SpringerLink Abstract 5/4/2011 http://www.springerlink.com/content/g4q40580h66705t8/