Hyperion – A Recursive Hyper-Heuristic Framework

Swan, Jerry; Özcan, Ender; Kendall, Graham

doi:10.1007/978-3-642-25566-3_48

Cited by 25 publications

(18 citation statements)

References 27 publications

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“…The Modified Choice Function controls the intensification and diversification parameters of the Choice Function automatically, using a method inspired by Reinforcement Learning. As the focus of that paper was on the comparison of two different selection Crossover is a core operator in many evolutionary algorithms, inspired by its biological namesake, and is now included as a low-level heuristic in many general purpose hyperheuristic frameworks such as HyFlex [23] and Hyperion [26]. In Genetic Algorithms, the canonical form of crossover combines two suitably fit solutions to yield a new solution which inherits genetic material from both.…”

Section: Literature Reviewmentioning

confidence: 99%

A modified choice function hyper-heuristic controlling unary and binary operators

Drake

Özcan

Burke

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Hyper-heuristics are a class of high-level search methodologies which operate on a search space of low-level heuristics or components, rather than on solutions directly. Traditional iterative selection hyper-heuristics rely on two key components, a heuristic selection method and a move acceptance criterion. Choice Function heuristic selection scores heuristics based on a combination of three measures, selecting the heuristic with the highest score. Modified Choice Function heuristic selection is a variant of the Choice Function which emphasises intensification over diversification within the heuristic search process. Previous work has shown that improved results are possible in some problem domains when using Modified Choice Function heuristic selection over the classic Choice Function, however in most of these cases crossover low-level heuristics (operators) are omitted. In this paper, we introduce crossover low-level heuristics into a Modified Choice Function selection hyper-heuristic and present results over six problem domains. It is observed that although on average there is an increase in performance when using crossover low-level heuristics, the benefit of using crossover can vary on a per-domain or per-instance basis.

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Section: Literature Reviewmentioning

confidence: 99%

A modified choice function hyper-heuristic controlling unary and binary operators

Drake

Özcan

Burke

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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“…adaptively). An equivalent formulation for recursion (with attendant aggregation facilities) has been implemented in the Hyperion hyper-heuristic solution-domain framework [12].…”

Section: Implications Of This Formulationmentioning

confidence: 99%

Searching the Hyper-heuristic Design Space

et al. 2013

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Abstract. We extend a previous mathematical formulation of hyperheuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with information that may also be exploited by other heuristics. Such a framework invites consideration of the kind of relaxations of the domain barrier that can be achieved without loss of generality. We give a concrete example of this architecture with an application to the 3-SAT domain that significantly improves on a related token-ring hyper-heuristic.

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“…Hyperion [46] and Hyper-heuristics Flexible Framework (HyFlex) [34] are software libraries for the implementation and comparison of different hyper/meta-heuristics. The HyFlex framework allows the implementation of hyper-heuristic approaches and supports reusability of hyper-heuristic components, providing an interface for the problem domains.…”

Section: First Cross-domain Heuristic Search Challenge (Chesc2011)mentioning

confidence: 99%

Memetic algorithms for Cross-domain Heuristic Search

Özcan

Asta

Altintaş

2013

2013 13th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Hyper-heuristic Flexible Framework (HyFlex) is an interface designed to enable the development, testing and comparison of iterative general-purpose heuristic search algorithms, particularly selection hyper-heuristics. A selection hyper-heuristic is a high level methodology that coordinates the interaction of a fixed set of low level heuristics (operators) during the search process. The Java implementation of HyFlex along with different problem domains was recently used in a competition, referred to as Cross-domain Heuristic Search Challenge (CHeSC2011). CHeSC2011 sought for the best selection hyper-heuristic with the best median performance over a set of instances from six different problem domains. Each problem domain implementation contained four different types of operators, namely mutation, ruin-recreate, hill climbing and crossover. CHeSC2011 including the competing hyper-heuristic methods currently serves as a benchmark for hyper-heuristic research. Considering the type of the operators implemented under the HyFlex framework, CHeSC2011 could also be used as a benchmark to empirically compare the performance of appropriate variants of the evolutionary computation methods across a variety of problem domains for discrete optimisation. In this study, we investigate the performance and generality level of generic steady-state and transgenerational memetic algorithms which hybridize genetic algorithms with hill climbing across six problem domains of the CHeSC2011 benchmark.

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Hyperion – A Recursive Hyper-Heuristic Framework

Cited by 25 publications

References 27 publications

A modified choice function hyper-heuristic controlling unary and binary operators

A modified choice function hyper-heuristic controlling unary and binary operators

Searching the Hyper-heuristic Design Space

Memetic algorithms for Cross-domain Heuristic Search

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