Balancing the selection pressures and migration schemes in parallel genetic algorithms for planning multiple paths
Sang-Keon Oh, Cheol Taek Kim, Ju-Jang Lee · 2002
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.