Knowledge discovery in multiobjective optimization problems in engineering via Genetic Programming
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- @Article{RUSSO:2018:ESA,
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author = "Igor L. S. Russo and Heder S. Bernardino and
Helio J. C. Barbosa",
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title = "Knowledge discovery in multiobjective optimization
problems in engineering via Genetic Programming",
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journal = "Expert Systems with Applications",
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year = "2018",
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volume = "99",
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pages = "93--102",
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month = "1 " # jun,
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keywords = "genetic algorithms, genetic programming, Innovization,
Multiobjective optimization, Knowledge discovery",
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ISSN = "0957-4174",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417417308230",
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DOI = "doi:10.1016/j.eswa.2017.12.008",
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abstract = "Recently, the interest of the researchers has grown in
post-optimality analyses, with the search for intrinsic
properties of the optimal solutions of a given problem.
Innovization has been defined as a process of knowledge
discovery, in the form of mathematical relationships
between variables, objectives, constraints, and
parameters, from the output of an optimization problem.
Genetic Programming (GP) is a bio-inspired
metaheuristic capable of automatically evolving
programs that can be used in this process. In spite of
its wide applicability, GP techniques can present some
issues when tackling knowledge discovery problems.
Here, three modifications are proposed in a GP
technique available in the literature for Innovization
problems: (i) a method to ensure the consistency of the
units of the principles using protected operations that
ignore invalid terms, (ii) a strategy to avoid trivial
solutions, and (iii) the use of an external archive to
store the solutions of interest found during the
search. Computational experiments are presented using
four engineering case studies (namely, a
two-member-truss, a welded beam, the cutting of a metal
part, and a composite gear) to verify the capacity of
the proposed GP method in finding design principles",
- }
Genetic Programming entries for
Igor L S Russo
Heder Soares Bernardino
Helio J C Barbosa
Citations