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Dynamic primitive granularity control: an exploration of unique design considerations

Published:08 July 2020Publication History

ABSTRACT

Dynamic primitive granularity control (DPGC) is a promising avenue for improving the performance of genetic programming (GP). However, it remains almost entirely unexplored. Further, it may pose many unique challenges in its design and implementation that traditional GP implementations do not. This paper presents an implementation of DPGC in order to determine what aspects of conventional GP design and implementation require special consideration. There are some common techniques used in GP that have been found here to negatively impact DPGC's ability to improve performance. Parsimony pressure appears to disproportionately penalize low-level primitives, and a mixed-granularity population suffers from heavy biases towards particular granularity levels, seemingly to the detriment of evolution. This paper provides hypotheses as to why these conventional techniques harm DPGC implementations, as well as several potential alternatives for use in the future that may remedy these detrimental effects.

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            cover image ACM Conferences
            GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
            July 2020
            1982 pages
            ISBN:9781450371278
            DOI:10.1145/3377929

            Copyright © 2020 ACM

            © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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            • Published: 8 July 2020

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