TY - GEN
T1 - Multi-Objective Structure and Parameter Evolution of Neuro-Fuzzy Systems
AU - Moshaiov, Amiram
AU - Salih, Adham
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzy systems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved solutions. The suggested algorithm, which is termed Fuzzy-Evolution of Membership and Structures by Decomposition (FEMS/D). As suggested by its name, the algorithm is based on the decomposition approach, which is a divide-and-conquer technique that transforms the original multi-objective problem into a set of single-objective subproblems. In addition, this paper provides initial demonstrations of the applicability of FEMS/D to the development of nondominated robot motion controllers, which exhibit a range of behaviors between the safest and the fastest ones. Finally, the adaptation of the controllers to an abrupt change in the environment is also demonstrated.
AB - This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzy systems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved solutions. The suggested algorithm, which is termed Fuzzy-Evolution of Membership and Structures by Decomposition (FEMS/D). As suggested by its name, the algorithm is based on the decomposition approach, which is a divide-and-conquer technique that transforms the original multi-objective problem into a set of single-objective subproblems. In addition, this paper provides initial demonstrations of the applicability of FEMS/D to the development of nondominated robot motion controllers, which exhibit a range of behaviors between the safest and the fastest ones. Finally, the adaptation of the controllers to an abrupt change in the environment is also demonstrated.
KW - Decomposition Approach
KW - Fuzzy-Inference System
KW - Many-objective Optimization
KW - Pareto-optimization
KW - Robot Motion-Control
UR - http://www.scopus.com/inward/record.url?scp=85125812755&partnerID=8YFLogxK
U2 - 10.1109/SSCI50451.2021.9659854
DO - 10.1109/SSCI50451.2021.9659854
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AN - SCOPUS:85125812755
T3 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
BT - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Y2 - 5 December 2021 through 7 December 2021
ER -