DocumentCode
1560974
Title
A novel dynamic population based evolutionary algorithm for revised multimodal function optimization problem
Author
Jun, Qin ; Li-shan, Kang
Author_Institution
Comput. Coll., South Center Univ. for Nat., Wuhan, China
Volume
3
fYear
2004
Firstpage
2288
Abstract
A revised definition about the task of multi-modal function optimization problem (called "rMOP"), which is to locate all optimal peaks including global and local optima, is presented. Then, a novel evolutionary algorithm aimed to rMOP with dynamic population (DPEA) is given. In DPEA, the initial population size is specified randomly. In the process of evolution, the size of population is tuned by a mechanism called "suppression" to delete crowded individuals and a process called "introduction of new individuals" to reinforce the global searching. Some experiments show that the population size of DPEA converges to the number of all peaks of the test function adaptively.
Keywords
evolutionary computation; functional analysis; search problems; dynamic population; evolution process; evolutionary algorithm; global searching process; revised multimodal function optimization problem; suppression mechanism; test function; Educational institutions; Evolutionary computation; Laboratories; Parallel processing; Stochastic processes; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN
0-7803-8273-0
Type
conf
DOI
10.1109/WCICA.2004.1341998
Filename
1341998
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