TY - GEN
T1 - Variation rate
T2 - 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
AU - Cuate, Oliver
AU - Schütze, Oliver
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In almost all cases the performance of a multi-objective evolutionary algorithm (MOEA) is measured in terms of its approximation quality in objective space. As a consequence, most MOEAs focus on such approximations while neglecting the distribution of the individuals in decision space. This, however, represents a potential shortcoming in certain applications as in many cases one can obtain the same or a very similar qualities (measured in objective space) in several ways (measured in decision space) which may be very valuable information for the decision maker for the realization of a project. In this work, we propose the variable-NSGA-III (vNSGA-III) an algorithm that performs an exploration both in objective and decision space. The idea behind this algorithm is the so-called variation rate, a heuristic that can easily be integrated into other MOEAs as it is free of additional design parameters. We demonstrate the effectiveness of our approach on several benchmark problems, where we show that, compared to other methods, we significantly improve the approximation quality in decision space without any loss in the quality in objective space.
AB - In almost all cases the performance of a multi-objective evolutionary algorithm (MOEA) is measured in terms of its approximation quality in objective space. As a consequence, most MOEAs focus on such approximations while neglecting the distribution of the individuals in decision space. This, however, represents a potential shortcoming in certain applications as in many cases one can obtain the same or a very similar qualities (measured in objective space) in several ways (measured in decision space) which may be very valuable information for the decision maker for the realization of a project. In this work, we propose the variable-NSGA-III (vNSGA-III) an algorithm that performs an exploration both in objective and decision space. The idea behind this algorithm is the so-called variation rate, a heuristic that can easily be integrated into other MOEAs as it is free of additional design parameters. We demonstrate the effectiveness of our approach on several benchmark problems, where we show that, compared to other methods, we significantly improve the approximation quality in decision space without any loss in the quality in objective space.
KW - Decision space diversity
KW - Evolutionary computation
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85063062773&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-12598-1_17
DO - 10.1007/978-3-030-12598-1_17
M3 - Contribución a la conferencia
AN - SCOPUS:85063062773
SN - 9783030125974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 203
EP - 215
BT - Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
A2 - Klamroth, Kathrin
A2 - Deb, Kalyanmoy
A2 - Goodman, Erik
A2 - Reed, Patrick
A2 - Miettinen, Kaisa
A2 - Mostaghim, Sanaz
A2 - Coello Coello, Carlos A.
PB - Springer Verlag
Y2 - 10 March 2019 through 13 March 2019
ER -