Simplifying Fitness Landscapes Using Dilation Functions Evolved With Genetic Programming
Created by W.Langdon from
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- @Article{Papetti:2023:CIM,
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author = "Daniele M. Papetti and Andrea Tangherloni and
Davide Farinati and Paolo Cazzaniga and Leonardo Vanneschi",
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journal = "IEEE Computational Intelligence Magazine",
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title = "Simplifying Fitness Landscapes Using Dilation
Functions Evolved With Genetic Programming",
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year = "2023",
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volume = "18",
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number = "1",
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pages = "22--31",
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abstract = "Several optimisation problems have features that
hinder the capabilities of searching heuristics. To
cope with this issue, different methods have been
proposed to manipulate search spaces and improve the
optimization process. This paper focuses on Dilation
Functions (DFs), which are one of the most promising
techniques to manipulate the fitness landscape, by
{"}expanding{"} or {"}compressing{"} specific regions.
The definition of appropriate DFs is problem dependent
and requires a-priori knowledge of the optimization
problem. Therefore, it is essential to introduce an
automatic and efficient strategy to identify optimal
DFs. With this aim, we propose a novel method based on
Genetic Programming, named GP4DFs, which is capable of
evolving effective DFs. GP4DFs identifies optimal
dilations, where a specific DF is applied to each
dimension of the search space. Moreover, thanks to a
knowledge-driven initialization strategy, GP4DFs
converges to better solutions with a reduced number of
fitness evaluations, compared to the state-of-the-art
approaches. The performance of GP4DFs is assessed on a
set of 43 benchmark functions mimicking several
features of real-world optimization problems. The
obtained results indicate the suitability of the
generated DFs.",
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keywords = "genetic algorithms, genetic programming, Evolution
(biology), Social factors, Transforms, Benchmark
testing, Search problems, Statistics",
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DOI = "doi:10.1109/MCI.2022.3222096",
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ISSN = "1556-6048",
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month = feb,
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notes = "Also known as \cite{10026153}",
- }
Genetic Programming entries for
Daniele M Papetti
Andrea Tangherloni
Davide Farinati
Paolo Cazzaniga
Leonardo Vanneschi
Citations