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HyFlex: a benchmark framework for cross-domain heuristic search

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TLDR
HyFlex as discussed by the authors is a software framework for the development of cross-domain search methodologies, which features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific.
Abstract
This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.

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Hyper-heuristics: a survey of the state of the art

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Recent Advances in Selection Hyper-heuristics

TL;DR: This paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research, and the existing classification of selectionhyper- heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research.
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A Classification of Hyper-Heuristic Approaches: Revisited

TL;DR: This chapter overviews previous categorisations of hyper-heuristics and provides a unified classification and definition of heuristic categories and distinguishes between two mainhyper-heuristic categories: heuristic selection and heuristic generation.
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Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems

TL;DR: A gene expression programming algorithm is proposed to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework, which generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.
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A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

TL;DR: The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration, instead of using human-designed criteria.
References
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Journal ArticleDOI

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

TL;DR: A simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics, and performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.
Journal ArticleDOI

Benchmarks for basic scheduling problems

TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.
Book ChapterDOI

Parameter Control in Evolutionary Algorithms

TL;DR: A classification of different approaches based on a number of complementary features is provided, and special attention is paid to setting parameters on-the-fly, which has the potential of adjusting the algorithm to the problem while solving the problem.
Journal ArticleDOI

A general heuristic for vehicle routing problems

TL;DR: A unified heuristic which is able to solve five different variants of the vehicle routing problem and shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger.
Book ChapterDOI

Iterated local search

TL;DR: Iterated Local Search (ILS) as mentioned in this paper is a general purpose metaheuristic for finding good solutions of combinatorial optimization problems, which is based on building a sequence of (locally optimal) solutions by perturbing the current solution and applying local search to that modified solution.
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