TY - GEN
T1 - Self-referential quality diversity through differential MAP-Elites
AU - Choi, Tae Jong
AU - Togelius, Julian
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1103138) and the National Science Foundation (NSF) award (No. 1717324).
Publisher Copyright:
© 2021 ACM.
PY - 2021/6/26
Y1 - 2021/6/26
N2 - Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.
AB - Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.
KW - Artificial intelligence
KW - Evolutionary algorithms
KW - Numerical optimization
KW - Quality-diversity algorithms
UR - http://www.scopus.com/inward/record.url?scp=85110189241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85110189241&partnerID=8YFLogxK
U2 - 10.1145/3449639.3459383
DO - 10.1145/3449639.3459383
M3 - Conference contribution
AN - SCOPUS:85110189241
T3 - GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference
SP - 502
EP - 509
BT - GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
T2 - 2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Y2 - 10 July 2021 through 14 July 2021
ER -