On the Importance of Phenotypic Duplicate Elimination in Decoder-Based Evolutionary Algorithms
Günther Robert Raidl, Jens Gottlieb · 2002
Premature convergence is a serious problem in many applications of evolutionary algorithms (EAs), since it decreases the EA’s chance to reach new high-quality regions of the search space and hence degrades the overall performance. In particular decoder-based EAs are frequently susceptible to premature convergence due to their encoding redundancy. Our comparison of four decoder-based EAs for the multidimensional knapsack problem reveals the importance of maintaining the population’s phenotypic diversity. We identify phenotypic duplicate elimination as a general method which efficiently prevents premature convergence for most EAs, while duplicate elimination on genotypic level is demonstrated as being unable to maintain phenotypic diversity. 1