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Optimization knowledge base: an open database for algorithm and problem characteristics and optimization results

Published: 07 July 2012 Publication History

Abstract

This paper describes the optimization knowledge base (OKB), a database for storing information about algorithms and problems. The optimization knowledge base allows to save results of algorithm executions as well as problem-specific information of fitness landscape analyses. This database can be queried and gives researchers a tool for gaining a better understanding of problems and algorithms and their behavior. Therefore the OKB supports parameter tuning and keeping track of tested algorithm and parameter settings as well as their results. Furthermore, the OKB and fitness landscape analysis can be used to not only explain the behavior of algorithms but to calculate similarities between problem instances and algorithms. Based on similarities and already captured knowledge, parameter settings can be extracted that could work well for new problem instances. Additionally, the OKB can be used to publish results of experiments for a broader audience, which advocates transparency of scientific work in the area of metaheuristics.

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Cited By

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  • (2018)Quasi-bistability of walk-based landscape measures in stochastic fitness landscapesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205471(1087-1094)Online publication date: 2-Jul-2018
  • (2018)Integrating Exploratory Landscape Analysis into Metaheuristic AlgorithmsComputer Aided Systems Theory – EUROCAST 201710.1007/978-3-319-74718-7_57(473-480)Online publication date: 26-Jan-2018
  • (2017)Optimising the Meta-Optimiser in Machine Learning ProblemsProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056613(15-22)Online publication date: 24-Feb-2017
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  1. Optimization knowledge base: an open database for algorithm and problem characteristics and optimization results

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    cover image ACM Conferences
    GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
    July 2012
    1586 pages
    ISBN:9781450311786
    DOI:10.1145/2330784
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2012

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    Author Tags

    1. fitness landscape analysis
    2. heuristiclab
    3. knowledge base
    4. metaheuristics
    5. parameter control
    6. parameter tuning

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    GECCO '12
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    GECCO '12: Genetic and Evolutionary Computation Conference
    July 7 - 11, 2012
    Pennsylvania, Philadelphia, USA

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    View all
    • (2018)Quasi-bistability of walk-based landscape measures in stochastic fitness landscapesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205471(1087-1094)Online publication date: 2-Jul-2018
    • (2018)Integrating Exploratory Landscape Analysis into Metaheuristic AlgorithmsComputer Aided Systems Theory – EUROCAST 201710.1007/978-3-319-74718-7_57(473-480)Online publication date: 26-Jan-2018
    • (2017)Optimising the Meta-Optimiser in Machine Learning ProblemsProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056613(15-22)Online publication date: 24-Feb-2017
    • (2016)Optimization Knowledge CenterProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931724(1331-1338)Online publication date: 20-Jul-2016

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