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