Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Dynamic Time-Linkage Problems Revisited
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Theoretical analysis of simple evolution strategies in quickly changing environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
DDD: A New Ensemble Approach for Dealing with Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Robust route optimization for gritting/salting trucks: a CERCIA experience
IEEE Computational Intelligence Magazine
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Metaheuristics for Dynamic Optimization
Metaheuristics for Dynamic Optimization
Finding robust solutions to dynamic optimization problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Dynamic optimisation has been studied for many years within the evolutionary computation community. Many strategies have been proposed to tackle the challenge, e.g., memory schemes, multiple populations, random immigrants, restart schemes, etc. This talk will first review a few of such strategies in dealing with dynamic optimisation. Then some less researched areas are discussed, including dynamic constrained optimisation, dynamic combinatorial optimisation, time-linkage problems, and theoretical analyses in dynamic optimisation. A couple of theoretical results, which were rather unexpected at the first sight, will be mentioned. Finally, a few future research directions are highlighted. In particular, potential links between dynamic optimisation and online learning are pointed out as an interesting and promising research direction in combining evolutionary computation with machine learning.