DocumentCode :
1879228
Title :
Novel composition test functions for numerical global optimization
Author :
Liang, J.J. ; Suganthan, P.N. ; Deb, K.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
fYear :
2005
fDate :
8-10 June 2005
Firstpage :
68
Lastpage :
75
Abstract :
In the evolutionary optimization field, there exist some algorithms taking advantage of the known property of the benchmark functions, such as local optima lying along the coordinate axes, global optimum having the same values for many variables and so on. Multiagent genetic algorithm (MAGA) is an example for this class of algorithms. In this paper, we identify shortcomings associated with the existing test functions. Novel hybrid benchmark functions, whose complexity and properties can be controlled easily, are introduced and several evolutionary algorithms are evaluated with the novel test functions.
Keywords :
evolutionary computation; optimisation; benchmark functions; composition test functions; evolutionary algorithm; evolutionary optimization; local optima; multiagent genetic algorithm; numerical global optimization; Benchmark testing; Evolutionary computation; Genetic algorithms; Mechanical engineering; Mechanical factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE
Print_ISBN :
0-7803-8916-6
Type :
conf
DOI :
10.1109/SIS.2005.1501604
Filename :
1501604
Link To Document :
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