Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Almutairi, Alhanof; Özcan, Ender; Kheiri, Ahmed; Jackson, Warren G.

doi:10.1007/978-3-319-47217-1_17

Cited by 6 publications

(17 citation statements)

References 13 publications

(26 reference statements)

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“…In addition, hyperheuristics [Ochoa et al, 2012] can also be seen as a form of DAC. In this field, it has been shown that dynamic heuristic selection outperforms static approaches on combinatorial optimization problems like Knapsack or Max-Cut [Almutairi et al, 2016].…”

Section: Related Workmentioning

confidence: 99%

DACBench: A Benchmark Library for Dynamic Algorithm Configuration

Eimer¹,

Biedenkapp²,

Reimer³

et al. 2021

Preprint

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Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical results have demonstrated the benefits of dynamically controlling hyperparameters in domains like evolutionary computation, AI Planning or deep learning. Replicating these results, as well as studying new methods for DAC, however, is difficult since existing benchmarks are often specialized and incompatible with the same interfaces. To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. For the design of DACBench, we focused on important desiderata, such as (i) flexibility, (ii) reproducibility, (iii) extensibility and (iv) automatic documentation and visualization. To show the potential, broad applicability and challenges of DAC, we explore how a set of six initial benchmarks compare in several dimensions of difficulty. 1 The project repository can be found at https://github.com/automl/DACBench

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Section: Related Workmentioning

confidence: 99%

DACBench: A Benchmark Library for Dynamic Algorithm Configuration

Eimer¹,

Biedenkapp²,

Reimer³

et al. 2021

Preprint

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“…Regarding hyperheuristics, several options can be found. HyFlex is a framework that aims to minimize the problem domain knowledge required to implement solvers, allowing us to focus the design effort on the construction of adaptive high‐level algorithms/strategies. This can be addressed by providing a number of problem domain modules, where each one encapsulates the problem‐specific algorithm components.…”

Section: Software Design Support For Heuristic Algorithmsmentioning

confidence: 99%

“…There is an emerging interest in the software design perspective of heuristic methods, including the software design elements within reusable libraries and the study of design patterns for metaheuristics . Several libraries and utilities have been reported in the literature to address software design issues of heuristic algorithms at the metaheuristic level and hyperheuristic level or for generic purposes . These alternatives are commonly constrained to the application of design patterns defined in specific‐purpose conceptual models, eg, evolutionary computation.…”

Section: Introductionmentioning

confidence: 99%

hMod: A software framework for assembling highly detailed heuristics algorithms

et al. 2019

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Summary Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod.

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“…Equation (1) is the objective function that calculates the sum of the weights of the objects in packing P. Equation (2) ensures that at most one object from set T j can be packed in packing P.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…The characteristics of the test instances are reported in Tables 7 to 9. In these three tables, for each instance, the columns with the headings Var, Cnst, Density, Max_One, and Weight show the number of objects (|I|), number of constraints (|J |), percentage of non-null elements in the constraint matrix, size of the largest set |T j | over all j ∈ {1, 2, ... , M}, and weight c i ∈ [1-1] or [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] of object i ∈ I, respectively. All the test instances can be downloaded from http://www3.inrets.fr/~delorme/Instancesfr.html.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

An evolutionary algorithm based hyper-heuristic framework for the set packing problem

Chaurasia

Kim

2019

Information Sciences

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights• An evolutionary algorithm based hyper-heuristic is proposed for the set packing problem (SPP) and the minimum weight dominating set (MWDS) problem.• Self-learning concept is employed in hyper-heuristic for a selection of heuristic.• Dynamic selection of parameter is adopted to make the approach, as much as possible, less dependent on parameter values. The empirical study shows the effectiveness of Dynamic selection of parameter.• The proposed approach has been compared with the respective state-of-the-art approaches for both the SPP and MWDS problem.• Computational results show the superiority of the proposed approach.

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Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Cited by 6 publications

References 13 publications

DACBench: A Benchmark Library for Dynamic Algorithm Configuration

DACBench: A Benchmark Library for Dynamic Algorithm Configuration

hMod: A software framework for assembling highly detailed heuristics algorithms

An evolutionary algorithm based hyper-heuristic framework for the set packing problem

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