• Controllable exploration of a...

Treffer: Controllable exploration of a design space via interactive quality diversity

Title:
Controllable exploration of a design space via interactive quality diversity
Authors:
Sfikas, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N., Genetic and Evolutionary Computation Conference Companion
Publisher Information:
Association for Computing Machinery
Publication Year:
2023
Collection:
University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms:
Evolutionary computation, Computer games -- Design, Algorithms, Application software, Artificial intelligence, Computer communication systems, Pattern recognition
Document Type:
Konferenz conference object
Language:
English
Relation:
https://www.um.edu.mt/library/oar/handle/123456789/121628
DOI:
10.1145/3583133.3590616
Availability:
https://www.um.edu.mt/library/oar/handle/123456789/121628
https://doi.org/10.1145/3583133.3590616
Rights:
info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number:
edsbas.C3BFE857
Database:
BASE

Weitere Informationen

This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternatives and their selections affect the upcoming results. We implement a variation of the MAP-Elites algorithm where the presented alternatives are sampled from a small region (window) of the behavioral space. After a user selection, the window is centered on the selected individual’s behavior characterization, evolution selects parents from within this window to produce offspring, and new alternatives are sampled. Essentially we define an adaptive system of local QD search, where the user’s selections guide the search towards specific regions of the behavioral space. The system is tested on the generation of architectural layouts, a constrained optimization task, leveraging QD search through a two-archive approach. ; peer-reviewed