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Data-efficient Neuroevolution with Kernel-Based Surrogate Models

  • Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.

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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):GECCO ’18: Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, Japan
First Page:85
Last Page:92
ISBN:978-1-4503-5618-3
DOI:https://doi.org/10.1145/3205455.3205510
ArXiv Id:http://arxiv.org/abs/1804.05364
Publisher:ACM
Date of first publication:2018/07/02
Award:Best Paper Award
Note:
This work received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 637972, project "ResiB bots") and the German Federal Ministry of Education and Research under the Forschung an Fachhochschulen mit Unternehmen programme (grant agreement number 03FH012PX5 project "Aeromat").
Tag:NEAT; Neuroevolution; Surrogate Modeling
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2018/05/04