MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7316

@InProceedings{Zhang:2023:EuroGP,

• author = "Hengzhe Zhang and Qi Chen and Alberto Tonda and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
• title = "{MAP}-Elites with Cosine-Similarity for Evolutionary Ensemble Learning",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "84--100",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Evolutionary ensemble learning, Quality diversity optimization, Multi-dimensional Archive of Phenotypic Elites",
• isbn13 = "978-3-031-29572-0",
• URL = "https://rdcu.be/c8UP0",
• DOI = "doi:10.1007/978-3-031-29573-7_6",
• size = "17 pages",
• abstract = "Evolutionary ensemble learning methods with Genetic Programming have achieved remarkable results on regression and classification tasks by employing quality-diversity optimization techniques like MAP-Elites and Neuro-MAP-Elites. The MAP-Elites algorithm uses dimensionality reduction methods, such as variational auto-encoders, to reduce the high-dimensional semantic space of genetic programming to a two-dimensional behavioral space. Then, it constructs a grid of high-quality and diverse models to form an ensemble model. In MAP-Elites, however, variational auto-encoders rely on Euclidean space topology, which is not effective at preserving high-quality individuals. To solve this problem, this paper proposes a principal component analysis method based on a cosine-kernel for dimensionality reduction. In order to deal with unbalanced distributions of good individuals, we propose a zero-cost reference points synthesizing method. Experimental results on 108 datasets show that combining principal component analysis using a cosine kernel with reference points significantly improves the performance of the MAP-Elites evolutionary ensemble learning algorithm.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Hengzhe Zhang Qi Chen Alberto Tonda Bing Xue Wolfgang Banzhaf Mengjie Zhang

Citations