Strongly-Typed Geometric Syntactic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.9219

@InProceedings{DBLP:conf/ppsn/SergioCM26,

• author = "Miguel Sergio and Ernesto Costa and Joao Macedo",
• editor = "Giovanni Iacca and Giorgia Nadizar and Anil Yaman and Doina Bucur and Antonio Della Cioppa and Ting Hu and Eric Medvet and Sarah L. Thomson",
• title = "Strongly-Typed Geometric Syntactic Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature - {PPSN} {XIX} - 19th International Conference, {PPSN} 2026, Trento, Italy, August 29 - September 2, 2026, Proceedings, Part {IV}",
• series = "Lecture Notes in Computer Science",
• volume = "16988",
• pages = "217--232",
• publisher = "Springer",
• year = "2026",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://doi.org/10.1007/978-3-032-36229-2_14",
• DOI = "10.1007/978-3-032-36229-2_14",
• timestamp = "Wed, 09 Sep 2026 18:09:11 +0200",
• biburl = "https://dblp.org/rec/conf/ppsn/SergioCM26.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• abstract = "Strongly-Typed Geometric Syntactic Genetic Programming (ST-GSynGP), an extension of Geometric Syntactic Genetic Programming (GSynGP), which is a tree-based Geometric GP variant that leverages geometric crossover in syntactic space to implicitly mitigate bloat and preserve locality. ST-GSynGP improves GSynGP's applicability for complex problems by accommodating strongly typed and variable-arity functions directly into the geometric crossover mechanism. ST-GSynGP is compared to Strongly-typed Genetic Programming (STGP) across symbolic regression, Boolean logic, classification, and control benchmarks. Experimental results demonstrate that ST-GSynGP achieves competitive performance while consistently producing more compact and interpretable solutions. These findings demonstrate that combining strong typing with geometric syntactic recombination provides an effective foundation for evolving high-performing and interpretable machine learning models for a wide range of applications.",
• notes = "https://ppsn2026.disi.unitn.it/",

}

Genetic Programming entries for Miguel Sergio Ernesto Costa Joao Macedo

Citations