Discovery of Search Objectives in Continuous Domains

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Liskowski:2017:GECCO,

• author = "Pawel Liskowski and Krzysztof Krawiec",
• title = "Discovery of Search Objectives in Continuous Domains",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4920-8",
• address = "Berlin, Germany",
• pages = "969--976",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3071178.3071344",
• DOI = "doi:10.1145/3071178.3071344",
• acmid = "3071344",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, machine learning, multiobjective optimization, nonnegative matrix factorization",
• month = "15-19 " # jul,
• abstract = "In genetic programming (GP), the outcomes of the evaluation phase can be represented as an interaction matrix, with rows corresponding to programs in a population and columns corresponding to tests that define a program synthesis task. Recent contributions on Discovery of Objectives via Clustering (DOC) and Discovery of Objectives by Factorization of interaction matrix (DOF) show that informative characterizations of programs can be automatically derived from interaction matrices in discrete domains and used as search objectives in multidimensional setting. In this paper, we propose analogous methods for continuous domains and compare them with conventional GP that uses tournament selection, Age-Fitness Pareto Optimization, and GP with epsilon-lexicase selection. Experiments show that the proposed methods are effective for symbolic regression, systematically producing better-fitting models than the two former baselines, and surpassing epsilon-lexicase selection on some problems. We also investigate the hybrids of the proposed approach with the baselines, concluding that hybridization of DOC with epsilon-lexicase leads to the best overall results.",
• notes = "Also known as \cite{Liskowski:2017:DSO:3071178.3071344} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec

Citations