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

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.