Open Access Repository

Implementing phenotypic plasticity with an adaptive generative representation

Ashlock, D, Ashlock, W and Montgomery, J ORCID: 0000-0002-5360-7514 2019 , 'Implementing phenotypic plasticity with an adaptive generative representation', in Proceedings of the 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) , Institute of Electrical and Electronics Engineers, United States, pp. 173-180 , doi: 10.1109/CIBCB.2019.8791496.

Full text not available from this repository.

Abstract

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 Type: Conference Publication
Authors/Creators:Ashlock, D and Ashlock, W and Montgomery, J
Keywords: evolutionary computation, phenotypic plasticity, solution representation, self-avoiding walk
Journal or Publication Title: Proceedings of the 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Publisher: Institute of Electrical and Electronics Engineers
DOI / ID Number: 10.1109/CIBCB.2019.8791496
Copyright Information:

Copyright 2019 IEEE

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP