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An adaptive generative representation for evolutionary computation
conference contribution
posted on 2023-05-23, 11:22 authored by Ashlock, D, Erin MontgomeryErin MontgomeryThis 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.
History
Publication title
Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC)Pagination
1578-1585ISBN
978-1-5090-0622-9Department/School
School of Information and Communication TechnologyPublisher
IEEEPlace of publication
USAEvent title
2016 IEEE Congress on Evolutionary Computation (CEC)Event Venue
Vancouver, CanadaDate of Event (Start Date)
2016-07-24Date of Event (End Date)
2016-07-29Rights statement
Copyright 2016 IEEERepository Status
- Restricted