Empirical Analysis of Locality, Heritability and Heuristic Bias in Evolutionary Algorithms: A Case Study for the Multidimensional Knapsack Problem

Günther R. Raidl, Jens Gottlieb
2005 Evolutionary Computation  
Five different representations and associated variation operators are studied in the context of a steady-state evolutionary algorithm (EA) for the multidimensional knapsack problem. Four of them are indirect decoder-based techniques, and the fifth is a direct encoding including heuristic initialization, repair, and local improvement. The complex decoders and the local improvement and repair strategies make it practically impossible to completely analyze such EAs in a fully theoretical way.
more » ... comparing the general performance of the EA variants on two benchmark suites, we present a hands-on approach for empirically analyzing important aspects of initialization, mutation, and crossover in an isolated fashion. Static, inexpensive measurements based on randomly created solutions are performed in order to quantify and visualize specific properties with respect to heuristic bias, locality, and heritability. These tests shed light onto the complex behavior of such EAs and point out reasons for good or bad performance. In addition, the proposed measures are also examined during actual EA runs, which gives further insight into dynamic aspects of evolutionary search and verifies the validity of the isolated static measurements. All measurements are described in a general way, allowing for an easy adaption to other representations and combinatorial problems.
doi:10.1162/106365605774666886 pmid:16297279 fatcat:e6bbkbg47ranzcpfilhu53vo3e