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An adaptive generative representation for evolutionary computation


Ashlock, D and Montgomery, J, An adaptive generative representation for evolutionary computation, Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 July 2016, Vancouver, Canada, pp. 1578-1585. ISBN 978-1-5090-0622-9 (2016) [Refereed Conference Paper]

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Copyright 2016 IEEE

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DOI: doi:10.1109/CEC.2016.7743977


This study introduces a novel generative representation that is able to modify its expression in response to admissibility constraints that unfold as solutions are generated. The effect is that this self-adaptation in expression makes many inadmissible structures impossible to encode. The resulting reduction in the effective size of the search space yields performance increases amounting to several orders of magnitude for some problems. In addition to defining and exploring the capabilities of the self-adaptive representation, a technique for biasing its expression with numerical weights that strongly influences which optima are located is introduced. This both permits enhancement of optima with desirable properties and permits the inclusion of domain knowledge to improve performance. The test problems used are the self-avoiding walk problem, a surrogate for RFID tag antenna design, and the Towers of Hanoi problem.

Item Details

Item Type:Refereed Conference Paper
Keywords:evolutionary computation, solution representation, self-avoiding walk, Towers of Hanoi, adaptive generative representation
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:111641
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-09-27
Last Modified:2018-02-28

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