DocumentCode :
1671856
Title :
Research in the Performance Assessment of Multi-objective Optimization Evolutionary Algorithms
Author :
Deng, Guoqiang ; Huang, Zhangcan ; Tang, Min
Author_Institution :
Wuhan Univ. of Technol., Wuhan
fYear :
2007
Firstpage :
915
Lastpage :
918
Abstract :
The use of evolutionary algorithms (EAs) for search and optimization tasks has become very popular in the last few years. Improving the existing algorithms or presenting new algorithms will necessarily refer to the performance assessment of these algorithms. Measuring the performance of algorithms has a basic issue: whether there exists a standard methodology that various multi-objective optimization evolutionary algorithms (MOEAs) can be directly compared. Unfortunately, researchers haven´t paid much attention to this issue. This paper reviews some of the most representative assessment methodologies used in the literature and then provides some useful suggestions and advices for researchers of algorithms.
Keywords :
evolutionary computation; optimisation; search problems; evolutionary algorithm; multiobjective optimization; performance assessment; Algorithm design and analysis; Benchmark testing; Binary codes; Design optimization; Evolutionary computation; Genetic algorithms; Measurement standards; Optimization methods; Search engines; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Circuits and Systems, 2007. ICCCAS 2007. International Conference on
Conference_Location :
Kokura
Print_ISBN :
978-1-4244-1473-4
Type :
conf
DOI :
10.1109/ICCCAS.2007.4348197
Filename :
4348197
Link To Document :
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