Degeneracy Reduction or Duplicate Elimination? An Analysis on the Performance of Attributed Grammatical Evolution with Lookahead to Solve the Multiple Knapsack Problem

Abstract

This paper analyzes the impact of having degenerate code and duplicate elimination in an attribute grammar with lookahead (AG+LA) approach, a recently proposed mapping process for Grammatical Evolution (GE) using attribute grammar (AG) with a lookahead feature to solve heavily constrained multiple knapsack problems (MKP). Degenerate code, as used in DNA, is code in which different codons can represent the same thing. Many developmental systems, such as (GE), use a degenerate encoding to help promote neutral mutations, that is, minor genetic changes that do not result in a phenotypic change. Early work on GE suggested that at least some level of degeneracy has a significant impact on the quality of search when compared to the system with none. Duplicate elimination techniques, as opposed to degenerate encoding, are employed in decoder-based Evolutionary Algorithms (EAs) to ensure that the newly generated solutions are not already contained in the current population. The results and analysis show that it is crucial to incorporate duplicate elimination to improve the performance of AG+LA. Reducing level of degeneracy is also important to improve search performance, specially for the large instances of the MKP.