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
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Genetic algorithms in time-dependent environments
Theoretical aspects of evolutionary computing
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Impact of Frequency and Severity on Non-Stationary Optimization Problems
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evolutionary optimization in spatio-temporal fitness landscapes
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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This work introduces a general mathematical framework for non-stationary fitness functions which enables the exact definition of certain problem properties. The properties' influence on the severity of the dynamics is analyzed and discussed. Various different classes of dynamic problems are identified based on the properties. Eventually, for an exemplary model search space and a (1, λ)-strategy, the interrelation of the offspring population size and the success rate is analyzed. Several algorithmic techniques for dynamic problems are compared for the different problem classes.