A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

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@Article{Pietropolli:2023:ESJ,

• author = "Gloria Pietropolli and Giuliamaria Menara and Mauro Castelli",
• title = "A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration",
• journal = "Emerging Science Journal",
• year = "2023",
• volume = "7",
• number = "4",
• month = aug,
• keywords = "genetic algorithms, genetic programming, Particle Swarm Optimization, PSO, Surrogate Models, Fitness Landscapes",
• ISSN = "2610-9182",
• URL = "https://www.ijournalse.org/index.php/ESJ/article/view/1815",
• URL = "https://www.ijournalse.org/index.php/ESJ/article/view/1815/pdf",
• DOI = "doi:10.28991/ESJ-2023-07-04-01",
• size = "15 pages",
• 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.",
• 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

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