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Discovery of search objectives in continuous domains

Published: 01 July 2017 Publication History

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.

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  • (2023)The Metric is the Message: Benchmarking Challenges for Neural Symbolic RegressionMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43421-1_10(161-177)Online publication date: 18-Sep-2023
  • (2018)A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase SelectionEvolutionary Computation10.1162/evco_a_00224(1-26)Online publication date: 10-May-2018
  • (2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018
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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
July 2017
1427 pages
ISBN:9781450349208
DOI:10.1145/3071178
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 2017

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Author Tags

  1. genetic programming
  2. machine learning
  3. multiobjective optimization
  4. nonnegative matrix factorization

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GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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View all
  • (2023)The Metric is the Message: Benchmarking Challenges for Neural Symbolic RegressionMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43421-1_10(161-177)Online publication date: 18-Sep-2023
  • (2018)A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase SelectionEvolutionary Computation10.1162/evco_a_00224(1-26)Online publication date: 10-May-2018
  • (2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018
  • (2017)Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic ProgrammingFoundations of Computing and Decision Sciences10.1515/fcds-2017-001742:4(339-358)Online publication date: 9-Dec-2017

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