Cooperative Co-Evolutionary GP for High Dimensional Problems

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@InProceedings{Rodriguez-Coayahuitl:2020:PPSN,

• author = "Lino Rodriguez-Coayahuitl and Alicia Morales-Reyes and Hugo J. Escalante and Carlos A. {Coello Coello}",
• title = "Cooperative Co-Evolutionary {GP} for High Dimensional Problems",
• booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II",
• year = "2020",
• editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann",
• volume = "12270",
• series = "LNCS",
• pages = "48--62",
• address = "Leiden, Holland",
• month = "7-9 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Evolutionary machine learning, Co-evolutionary algorithms, Ensemble methods, Image processing",
• isbn13 = "978-3-030-58114-5",
• DOI = "doi:10.1007/978-3-030-58115-2_4",
• abstract = "We propose a framework for Cooperative Co-Evolutionary Genetic Programming (CCGP) that considers co-evolution at three different abstraction levels: genotype, feature and output level. A thorough empirical evaluation is carried out on a real-world high dimensional ML problem (image denoising). Results indicate that GP performance is enhanced only when cooperation happens at an output level (ensemble-alike). The proposed co-evolutionary ensemble approach is compared against a canonical GP implementation and a GP customized for image processing tasks. Preliminary results show that the proposed framework obtains superior average performance in comparison to the other GP models. Our most relevant finding is the empirical evidence showing that the proposed CCGP model is a promising alternative to specialized GP implementations that require knowledge of the problem domain.",
• notes = "Also known as \cite{DBLP:conf/ppsn/Rodriguez-Coayahuitl20} PPSN2020",

}

Genetic Programming entries for Lino Rodriguez-Coayahuitl Alicia Morales-Reyes Hugo Jair Escalante Carlos Artemio Coello Coello

Citations