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Implementing phenotypic plasticity with an adaptive generative representation


Ashlock, D and Ashlock, W and Montgomery, J, Implementing phenotypic plasticity with an adaptive generative representation, Proceedings of the 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 9-11 July 2019, Siena, Italy, pp. 173-180. ISBN 978-1-7281-1462-0 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 IEEE

DOI: doi:10.1109/CIBCB.2019.8791496


This study compares an adaptive and a nonadaptive representation for finding long walks on obstructed grids. This process models adaption of a simple plant to an environment where the plant's ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.

Item Details

Item Type:Refereed Conference Paper
Keywords:evolutionary computation, phenotypic plasticity, solution representation, self-avoiding walk
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:134914
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2019-09-12
Last Modified:2020-05-15

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