- author = "Daniel Ashlock and Wendy Ashlock and James Montgomery",
- title = "Implementing Phenotypic Plasticity with an Adaptive Generative Representation",
- booktitle = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)",
- year = "2019",
- address = "Siena, Italy",
- month = "9-11 " # jul,
- keywords = "genetic algorithms, genetic programming, SAW, AGR, agent",
- isbn13 = "978-1-7281-1463-7",
-
URL = "
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8791496",
-
DOI = "
doi:10.1109/CIBCB.2019.8791496",
- size = "8 pages",
- abstract = "This study compares an adaptive and a non-adaptive representation for finding long walks on obstructed grids. This process models adaptation 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.",
-
notes = "Is this GP? cites Ashlock:2016:CEC
Also known as \cite{8791496}",
