Hyper-Heuristics with Low Level Parameter Adaptation

Abstract

Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.

Learning from evolved next release problem instances

“…We also investigate the possibility of predicting the performance of the target heuristic over the evolved NRP instances. For the future work, we are interested in leveraging the problemspecific features to guide the algorithm design process, using approaches such as hyper-heuristics [5].…”

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

“…Moreover, since the size of the set of candidate low-level approaches is generally fixed and finite, in the case of controlling numeric parameters, the number of possible values that can be assigned to the numeric parameters is therefore also finite 1 . Despite this, hyper-heuristics have successfully been applied as adaptive parameter control techniques, both to benchmark problems (Ren et al, 2012) and to real world applications (Segura et al, 2013c).…”

“…Although a recent publication attempts to address this with a hyper-heuristic that is able to adapt the parameters of the low-level heuristics(Ren et al, 2012).…”

Hyper-heuristics with penalty parameter adaptation for constrained optimization

“…Recently, observing that the parameterization of the low-level heuristics poses great challenges to hyper-heuristics, Ren et al [24] developed a hyper-heuristic framework with adaptive low-level parameters. In the framework, high-level search consists of two modules for managing the low-level heuristics and the low-level parameters respectively and simultaneously.…”