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
Link To Document :
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