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Balancing the selection pressures and migration schemes in parallel genetic algorithms for planning multiple paths

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3 Author(s)
Sang-Keon Oh ; Dept. of Electr. Eng. & Comput. St., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea ; Cheol Taek Kim ; Ju-Jang Lee

Parallel genetic algorithms are particularly easy to implement and promise substantial gains in performance. Its basic idea is to keep several sub-populations that are processed by genetic algorithms. Furthermore, a migration mechanism produces a chromosome exchange between sub-population. In this paper, a new selection method based on nonlinear fitness assignment is presented. The use of the proposed ranking selection permits higher local exploitation search, where the diversity of population is maintained by a parallel sub-population structure. Experimental results show the relation between the local-global search balance and probabilities of reaching the desired solutions using test functions and nonstationary route-planning problems.

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Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on  (Volume:4 )

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