Heuristic space diversity control for improved meta-hyper-heuristic performance
Council for Scientific and Industrial Research, Pretoria, South Africa; Department of Computer Science, University of Pretoria, Pretoria, South Africa; School of Computer Science, University of Nottingham, United Kingdom; University of Nottingham, Malaysia Campus, Malaysia; Department of Industrial and Systems Engineering, University of Pretoria, Pretoria, South Africa
This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm. © 2014 Elsevier Inc. All rights reserved.
Algorithms; Digital arithmetic; Heuristic algorithms; Heuristic methods; Algorithm performance; Bench-mark problems; Diversity managements; Hyper-heuristic algorithms; Hyper-heuristics; Performance benefits; Population-based algorithm; Space diversity; Benchmarking