id: 06058970
dt: a
an: 06058970
au: Chen, Yu; Zhang, Kai; Zou, Xiufen
ti: A population-based hybrid extremal optimization algorithm.
so: Huang, De-Shuang (ed.) et al., Bio-inspired computing and applications. 7th
    international conference on intelligent computing, ICIC 2011,
    Zhengzhou, China, August 11‒14. 2011. Revised selected papers.
    Berlin: Springer (ISBN 978-3-642-24552-7/pbk). Lecture Notes in
    Computer Science 6840. Lecture Notes in Bioinformatics, 410-417 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: evolutionary algorithm; extremal optimization; memetic algorithm
ci: 
li: doi:10.1007/978-3-642-24553-4_54
ab: Summary: The extremal optimization (EO) algorithm is a kind of evolutionary
    algorithm which has been applied successfully in combinatorial
    optimization, while its application on continuous optimization
    encounters the problems of heavy complexity and weak exploration
    ability. This paper proposes a new hybrid population-based EO
    algorithm, named as the adaptive co-evolution population-based extremal
    optimization (ACPEO) algorithm, in which all individuals co-evolve
    adaptively with each other and the differential evolution (DE) operator
    is incorporated to improve the global search ability. By employing a
    novel evaluation method of variables, the ACPEO algorithm performs well
    on several kind of benchmark problems. Experimental results show that
    the ACPEO algorithm is robust due to the capability for solving
    different problems with the same parameter setting, and it is also
    stable because changes in the parameters’ values do not influence its
    performances seriously.
rv: