A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration
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- @Article{Pietropolli:2023:ESJ,
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author = "Gloria Pietropolli and Giuliamaria Menara and
Mauro Castelli",
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title = "A Genetic Programming Based Heuristic to Simplify
Rugged Landscapes Exploration",
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journal = "Emerging Science Journal",
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year = "2023",
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volume = "7",
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number = "4",
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month = aug,
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keywords = "genetic algorithms, genetic programming, Particle
Swarm Optimization, PSO, Surrogate Models, Fitness
Landscapes",
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ISSN = "2610-9182",
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URL = "https://www.ijournalse.org/index.php/ESJ/article/view/1815",
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URL = "https://www.ijournalse.org/index.php/ESJ/article/view/1815/pdf",
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DOI = "doi:10.28991/ESJ-2023-07-04-01",
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size = "15 pages",
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abstract = "a fundamental property of the original rugged fitness
landscape: the location of the global optimum. To
create such a surrogate model, we consider a linear
genetic programming approach coupled with a self-tuning
fitness function. More specifically, to evaluate the
fitness of the produced surrogate functions, we employ
Fuzzy Self-Tuning Particle Swarm Optimization, a
setting-free version of particle swarm optimization. To
assess the performance of the proposed method, we
considered a set of benchmark functions characterized
by high noise and ruggedness. Moreover, the method is
evaluated over different problems dimensionalities. The
proposed approach reveals its suitability for
performing the proposed task. In particular,
experimental results confirm its capability to find the
global argminimum for all the considered benchmark
problems and all the domain dimensions taken into
account, thus providing an innovative and promising
strategy for dealing with challenging optimization
problems.",
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notes = "Dipartimento di Matematica e Geoscienze, Universita
degli Studi di Trieste, H2bis Building, Via Alfonso
Valerio 12/1, 34127 Trieste, Italy.",
- }
Genetic Programming entries for
Gloria Pietropolli
Giuliamaria Menara
Mauro Castelli
Citations