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    Searching for novel regression functions

    Martinez, Yuliana and Naredo, Enrique and Trujillo, Leonardo and Galvan-Lopez, Edgar (2013) Searching for novel regression functions. IEEE Transactions on Evolutionary Computation. pp. 16-23. ISSN 1089-778X

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    The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness function in evolutionary computation. Some researchers have attempted to bridge this difference by reducing the need for an explicit fitness function. A noteworthy example is the novelty search (NS) algorithm, that substitutes fitness with a measure of uniqueness, or novelty, that each individual introduces into the search. NS employs the concept of behavioral space, where each individual is described by a domain-specific descriptor that captures the main features of an individual's performance. However, defining a behavioral descriptor is not trivial, and most works with NS have focused on robotics. This paper is an extension of recent attempts to expand the application domain of NS. In particular, it represents the first attempt to apply NS on symbolic regression with genetic programming (GP). The relationship between the proposed NS algorithm and recent semantics-based GP algorithms is explored. Results are encouraging and consistent with recent findings, where NS achieves below average performance on easy problems, and achieves very good performance on hard problems. In summary, this paper presents the first attempt to apply NS on symbolic regression, a continuation of recent research devoted at extending the domain of competence for behavior-based search.

    Item Type: Article
    Keywords: Novelty Search; Behavior-based Search; Genetic Programming; Symbolic Regression;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15377
    Identification Number:
    Depositing User: Edgar Galvan
    Date Deposited: 31 Jan 2022 16:55
    Journal or Publication Title: IEEE Transactions on Evolutionary Computation
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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