On Chromosome Crossover in Multimodal Adaptive Graph Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8506

@InProceedings{de-la-torre:2025:GECCOcomp2,

• author = "Camilo {De La Torre} and Sylvain Cussat-Blanc and Herve Luga and Dennis G. Wilson and Yuri Lavinas",
• title = "On Chromosome Crossover in Multimodal Adaptive Graph Evolution",
• booktitle = "Graph-based Genetic Programming",
• year = "2025",
• editor = "Roman Kalkreuth and Yuri Lavinas and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Dennis G. Wilson",
• pages = "2162--2166",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation, program synthesis",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734323",
• DOI = "doi:10.1145/3712255.3734323",
• size = "5 pages",
• abstract = "Crossover operators have historically shown limited effectiveness in Cartesian Genetic Programming (CGP), with mutation-only approaches typically dominating the evolutionary process. In this paper, we explore a natural crossover mechanism enabled by Multimodal Adaptive Graph Evolution (MAGE), a multi-chromosome generalization of CGP that groups functions by return type and constrains graph mutation based on type coherence. MAGE's distinct architecture creates an opportunity for a chromosome-level crossover operator that was not previously feasible in traditional CGP. We implement and evaluate this crossover approach on selected problems from the Second Program Synthesis Benchmark Suite (PSB2), comparing it against a mutation-only strategy. Our experiments reveal that chromosome-level crossover offers mixed effectiveness in MAGE, with promising exceptions in problems like GCD and Twitter. This work continues the inquiry into effective crossover methods in graph-based genetic programming, revealing how effectiveness is problem dependent.",
• notes = "GECCO-2025 GGP workshop A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Camilo De La Torre Sylvain Cussat-Blanc Herve Luga Dennis G Wilson Yuri Lavinas

Citations