Exploiting Landscape Information to Avoid Premature Convergence in Evolutionary Search

Premature convergence to suboptimal solutions is one of the prime concerns of using evolutionary algorithms (EA) in high complexity real world optimization problems. As the evolutionary search progresses, it is important to avoid reaching a state where the genetic operators can no longer produce superior offspring while striking a balance between exploration and exploitation. This is likely to occur when the search space reaches a homogeneous or near-homogeneous configuration. Maintaining a certain level of diversity is widely believed to help curb this problem. In [13], we presented an informed operator-based technique to maintain constructive diversity. The current work is an improvement on the COMMUNITY_GA model [13]. In addition to informed exploration of the COMMUNITY_GA, the current model balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. In addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, all the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance.