TY - GEN
T1 - Automatic adaptation of hypermutation rates for multimodal optimisation
AU - Corus, Dogan
AU - Oliveto, Pietro S.
AU - Yazdani, Donya
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/6
Y1 - 2021/9/6
N2 - Previous work has shown that in Artificial Immune Systems (AIS) the best static mutation rates to escape local optima with the ageing operator are far from the optimal ones to do so via large hyper-mutations and vice-versa. In this paper we propose an AIS that automatically adapts the mutation rate during the run to make good use of both operators. We perform rigorous time complexity analyses for standard multimodal benchmark functions with significant characteristics and prove that our proposed algorithm can learn to adapt the mutation rate appropriately such that both ageing and hypermutation are effective when they are most useful for escaping local optima. In particular, the algorithm provably adapts the mutation rate such that it is efficient for the problems where either operator has been proven to be effective in the literature.
AB - Previous work has shown that in Artificial Immune Systems (AIS) the best static mutation rates to escape local optima with the ageing operator are far from the optimal ones to do so via large hyper-mutations and vice-versa. In this paper we propose an AIS that automatically adapts the mutation rate during the run to make good use of both operators. We perform rigorous time complexity analyses for standard multimodal benchmark functions with significant characteristics and prove that our proposed algorithm can learn to adapt the mutation rate appropriately such that both ageing and hypermutation are effective when they are most useful for escaping local optima. In particular, the algorithm provably adapts the mutation rate such that it is efficient for the problems where either operator has been proven to be effective in the literature.
KW - ageing
KW - artificial immune systems
KW - evolutionary algorithms
KW - hypermutations
KW - multimodal optimization
KW - parameter adaptation
KW - randomized search heuristics
KW - runtime analysis
UR - http://www.scopus.com/inward/record.url?scp=85114941882&partnerID=8YFLogxK
U2 - 10.1145/3450218.3477305
DO - 10.1145/3450218.3477305
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:85114941882
T3 - FOGA 2021 - Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
BT - FOGA 2021 - Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
PB - Association for Computing Machinery, Inc
T2 - 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2021
Y2 - 6 September 2021 through 8 September 2021
ER -