DocumentCode :
1526419
Title :
Markov Models for Biogeography-Based Optimization
Author :
Simon, Dan ; Ergezer, Mehmet ; Du, Dawei ; Rarick, Rick
Author_Institution :
Dept. of Electr. & Comput. Eng., Cleveland State Univ., Cleveland, OH, USA
Volume :
41
Issue :
1
fYear :
2011
Firstpage :
299
Lastpage :
306
Abstract :
Biogeography-based optimization (BBO) is a population-based evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the science and study of the geographical distribution of biological organisms. In BBO, problem solutions are analogous to islands, and the sharing of features between solutions is analogous to the migration of species. This paper derives Markov models for BBO with selection, migration, and mutation operators. Our models give the theoretically exact limiting probabilities for each possible population distribution for a given problem. We provide simulation results to confirm the Markov models.
Keywords :
Markov processes; ecology; evolutionary computation; mathematical operators; Markov model; biogeography based optimization; biological organism; geographical distribution; mutation operator; population based evolutionary algorithm; probability distribution; Biogeography; Biological system modeling; Biological systems; Convergence; Evolutionary computation; Genetic algorithms; Genetic mutations; H infinity control; Mathematics; Simulated annealing; Biogeography-based optimization (BBO); Markov models; evolutionary algorithms (EAs); Algorithms; Biological Evolution; Computer Simulation; Cybernetics; Geography; Markov Chains; Models, Biological;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2010.2051149
Filename :
5497206
Link To Document :
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