Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of Its Solvers View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-08-21

AUTHORS

Ivan Amaya , José Carlos Ortiz-Bayliss , Santiago Enrique Conant-Pablos , Hugo Terashima-Marín , Carlos A. Coello Coello

ABSTRACT

Solvers for different combinatorial optimization problems have evolved throughout the years. These can range from simple strategies such as basic heuristics, to advanced models such as metaheuristics and hyper-heuristics. Even so, the set of benchmark instances has remained almost unaltered. Thus, any analysis of solvers has been limited to assessing their performance under those scenarios. Even if this has been fruitful, we deem necessary to provide a tool that allows for a better study of each available solver. Because of that, in this paper we present a tool for assessing the strengths and weaknesses of different solvers, by tailoring a set of instances for each of them. We propose an evolutionary-based model and test our idea on four different basic heuristics for the 1D bin packing problem. This, however, does not limit the scope of our proposal, since it can be used in other domains and for other solvers with few changes. By pursuing an in-depth study of such tailored instances, more relevant knowledge about each solver can be derived. More... »

PAGES

373-384

References to SciGraph publications

  • 2017. Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants in EVOLUTIONARY COMPUTATION IN COMBINATORIAL OPTIMIZATION
  • 2011-06. MIPLIB 2010 in MATHEMATICAL PROGRAMMING COMPUTATION
  • 2018-06. Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems in GENETIC PROGRAMMING AND EVOLVABLE MACHINES
  • 2011-02. Discovering the suitability of optimisation algorithms by learning from evolved instances in ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
  • 1990-11. OR-Library: Distributing Test Problems by Electronic Mail in JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • 2010. Understanding TSP Difficulty by Learning from Evolved Instances in LEARNING AND INTELLIGENT OPTIMIZATION
  • Book

    TITLE

    Parallel Problem Solving from Nature – PPSN XV

    ISBN

    978-3-319-99258-7
    978-3-319-99259-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-99259-4_30

    DOI

    http://dx.doi.org/10.1007/978-3-319-99259-4_30

    DIMENSIONS

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