A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic

Rivers, Rebecca; Tauritz, Daniel R.

doi:10.1145/2598394.2609872

Cited by 13 publications

(2 citation statements)

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“…Burke et al described a high-level approach to evolving heuristics [1]. That approach was extended to evolve entire BBSAs of indiscriminate type [11,12]. The research in this paper builds upon this work by analyzing the advantages and disadvantages of increasing the primitive-space the hyper-heuristic has access to.…”

Section: Related Workmentioning

confidence: 99%

Hyper-Heuristics

Rivers

Tauritz

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Practitioners often need to solve real world problems for which no custom search algorithms exist. In these cases they tend to use general-purpose solvers that have no guarantee to perform well on their specific problem. The relatively new field of hyper-heuristics provides an alternative to the potential pit-falls of general-purpose solvers, by allowing practitioners to generate a custom algorithm optimized for their problem of interest. Hyper-heuristics are meta-heuristics operating on algorithm space employing targeted primitives to compose algorithms. This paper explores the advantages and disadvantages of expanding a hyperheuristic's primitive-space with additional primitives. This should allow for an increase in quality of evolved algorithms. However, increasing the search space of a meta-heuristic almost always results in longer time to convergence and lower quality results for the same amount of computational time, but also all too often lower quality results at convergence, potentially making a problem impractical to solve for a practitioner. This paper explores the scalability of hyperheuristics as the primitive-space is increased, demonstrating significantly increased quality solutions at convergence with a corresponding increase in convergence time. Additionally, this paper explores the impact that the nature of the added primitives have on the performance of the hyper-heuristic.

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Section: Related Workmentioning

confidence: 99%

Hyper-Heuristics

Rivers

Tauritz

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…This poster paper summarizes the experiments detailed in [4] where the related work and a detailed description of the methodology and experiments can be found.…”

Section: Introductionmentioning

confidence: 99%

Multi-sample evolution of robust black-box search algorithms

Rivers

Tauritz

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from overspecialization. This poster paper presents a second generation hyperheuristic employing a multi-sample training approach to alleviate the overspecialization problem. A variety of experiments demonstrated the significant increase in the robustness of the generated algorithms due to the multi-sample approach, clearly showing its ability to outperform established BBSAs. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.

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