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
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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 Dynamic Severity and Population Size
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Supporting Polyploidy in Genetic Algorithms Using Dominance Vectors
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Computers & Mathematics with Applications
Experimental analysis of binary differential evolution in dynamic environments
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Immune-based algorithms for dynamic optimization
Information Sciences: an International Journal
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
Performance Measures for Dynamic Multi-Objective Optimization
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae
The parallel single front genetic algorithm (PSFGA) in dynamic multi-objective optimization
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A classification of dynamic multi-objective optimization problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A social behaviour evolution approach for evolutionary optimisation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Solving dynamic constrained optimization problems with asynchronous change pattern
EvoApplications'11 Proceedings of the 2011 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
How long should we run in dynamic optimization?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
An algorithm comparison for dynamic optimization problems
Applied Soft Computing
Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae
A benchmark generator for dynamic permutation-encoded problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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This article investigates systematically the utility of performance measures in non-stationary environments. Three characteristics for describing the goals of a dynamic adaptation process are proposed: accuracy, stability, and recovery. This examination underpins the usage of the best fitness value as a basis for measuring the three characteristics in scenarios with moderate changes of the best fitness value. However, for dynamic problems without coordinate transformations all considered fitness based measures exhibit severe problems. In case of the recovery, a newly proposed window based performance measure is shown to be best as long as the accuracy level of the optimization is rather high.