TY - GEN
T1 - Termination detection strategies in evolutionary algorithms
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
AU - Liu, Yanfeng
AU - Zhou, Aimin
AU - Zhang, Hu
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper provides an overview of developments on termination conditions in evolutionary algorithms (EAs). It seeks to give a representative picture of the termination conditions in EAs over the past decades, segment the contributions of termination conditions into progress indicators and termination criteria. With respect to progress indicators, we consider a variety of indicators, in particular in convergence indicators and diversity indicators. With respect to termination criteria, this paper reviews recent research on threshold strategy, statistical inference, i.e., Kalman filters, as well as Fuzzy methods, and other methods. Key developments on termination conditions over decades include: (i) methods of judging the algorithm's search behavior based on statistics, and (ii) methods of detecting the termination based on different distance formulations.
AB - This paper provides an overview of developments on termination conditions in evolutionary algorithms (EAs). It seeks to give a representative picture of the termination conditions in EAs over the past decades, segment the contributions of termination conditions into progress indicators and termination criteria. With respect to progress indicators, we consider a variety of indicators, in particular in convergence indicators and diversity indicators. With respect to termination criteria, this paper reviews recent research on threshold strategy, statistical inference, i.e., Kalman filters, as well as Fuzzy methods, and other methods. Key developments on termination conditions over decades include: (i) methods of judging the algorithm's search behavior based on statistics, and (ii) methods of detecting the termination based on different distance formulations.
KW - Evolutionary Algorithms
KW - Progress Indicators
KW - Termination Conditions
KW - Termination Criteria
UR - https://www.scopus.com/pages/publications/85050580867
U2 - 10.1145/3205455.3205466
DO - 10.1145/3205455.3205466
M3 - 会议稿件
AN - SCOPUS:85050580867
T3 - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
SP - 1063
EP - 1070
BT - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
Y2 - 15 July 2018 through 19 July 2018
ER -