The entity-to-algorithm allocation problem: Extending the analysis

Abstract

This paper extends the investigation into the algorithm selection problem in hyper-heuristics, otherwise referred to as the entity-to-algorithm allocation problem, introduced by Grobler et al.. Two newly developed population-based portfolio algorithms (the evolutionary algorithm based on self-adaptive learning population search techniques (EEA-SLPS) and the Multi-EA algorithm) are compared to two metahyper-heuristic algorithms. The algorithms are evaluated under similar conditions and the same set of constituent algorithms on a diverse set of floating-point benchmark problems. One of the meta-hyper-heuristics are shown to outperform the other algorithms, with EEA-SLPS coming in a close second.