Online Discovery of Search Objectives for Test-Based Problems
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gp-bibliography.bib Revision:1.8010
- @Article{Liskowski:2017:EC,
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author = "Pawel Liskowski and Krzysztof Krawiec",
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title = "Online Discovery of Search Objectives for Test-Based
Problems",
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journal = "Evolutionary Computation",
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year = "2017",
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volume = "25",
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number = "3",
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pages = "375--406",
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month = "Fall",
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keywords = "genetic algorithms, genetic programming, Coevolution,
test-based problems, multi-objective evolutionary
computation, search driver",
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ISSN = "1063-6560",
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URL = "https://doi.org/10.1162/evco_a_00179",
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DOI = "doi:10.1162/evco_a_00179",
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size = "32 pages",
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abstract = "In test-based problems, commonly approached with
competitive coevolutionary algorithms, the fitness of a
candidate solution is determined by the outcomes of its
interactions with multiple tests. Usually, fitness is a
scalar aggregate of interaction outcomes, and as such
imposes a complete order on the candidate solutions.
However, passing different tests may require unrelated
skills, and candidate solutions may vary with respect
to such capabilities. In this study, we provide
theoretical evidence that scalar fitness, inherently
incapable of capturing such differences, is likely to
lead to premature convergence. To mitigate this
problem, we propose disco, a method that automatically
identifies the groups of tests for which the candidate
solutions behave similarly and define the above skills.
Each such group gives rise to a derived objective, and
these objectives together guide the search algorithm in
multi-objective fashion. When applied to several
well-known test-based problems, the proposed approach
significantly outperforms the conventional
two-population coevolution. This opens the door to
efficient and generic countermeasures to premature
convergence for both coevolutionary and evolutionary
algorithms applied to problems featuring aggregating
fitness functions.",
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notes = "PMID: 26953882",
- }
Genetic Programming entries for
Pawel Liskowski
Krzysztof Krawiec
Citations