A Multi-Population Genetic Algorithm and Its Application to Design of Manipulators
Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 279 - 286, July, 1992
Abstract
In this paper, we introduce a new algorithm called Multi-Population Genetic Algorithm (MPGA) as an effi- cient optimization technique for highly nonlinear problems. Our MPGA is a parallel implementation of a GA and shows its robustness for our problem domain (Task Based Design; TBD) where we design a manipulator which is best suited for a given task. The MPGA has the same number of optimization fuac- tions as the number of task points and maintains almost con- stant complexity and does not depend on the number of task points. In addition, we develop a framework called Progressive Design, in which we design progressively based on coarse-6ne approach.
BibTeX
@conference{Kim-1992-13400,author = {Jin-Oh Kim and Pradeep Khosla},
title = {A Multi-Population Genetic Algorithm and Its Application to Design of Manipulators},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {1992},
month = {July},
pages = {279 - 286},
}
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