Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Fitness landscapes and evolvability
Evolutionary Computation
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Optimum tracking with evolution strategies
Evolutionary Computation
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Learning behavior in abstract memory schemes for dynamic optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on ICNC-FSKD’2008;Guest Editors: Liang Zhao, Maozu Guo, Lipo Wang
Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Memory based on abstraction for dynamic fitness functions
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Associative memory scheme for genetic algorithms in dynamic environments
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Measuring fitness degradation in dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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We consider optimization problems with a dynamic fitness landscape and dynamic constraints that may change independent of each other in terms of their respective time regimes. This generally leads to asynchronous change pattern with the possibility of occasional synchronization points. We present a framework for describing such a dynamical setting and for performing numerical experiments on the algorithm's behavior.