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Expressivity of parameterized and data-driven representations in quality diversity search

  • We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions.

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Document Type:Conference Object
Author:Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck
Parent Title (English):Chicano, Krawiec (Eds.): GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France, 10-14 July, 2021
Number of pages:9
First Page:678
Last Page:686
ArXiv Id:http://arxiv.org/abs/2105.04247
Publisher:Association for Computing Machinery
Place of publication:New York, NY, USA
Date of first publication:2021/06/26
Copyright:© 2021 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. Abstracting with credit is permitted.
Keyword:Generative Models; Quality diversity; Variational Autoencoder
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:2021/06/30