Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Differential Evolution with Noise Analyzer
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Prudent-Daring vs tolerant survivor selection schemes in control design of electric drives
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IEEE Transactions on Evolutionary Computation
Self-adaptive mutations may lead to premature convergence
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A general noise model and its effects on evolution strategy performance
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
IEEE Transactions on Evolutionary Computation
Compact Particle Swarm Optimization
Information Sciences: an International Journal
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This paper proposes the Noise Analysis compact Genetic Algorithm (NAcGA). This algorithm integrates a noise analysis component within a compact structure. This fact makes the proposed algorithm appealing for those real-world applications characterized by the necessity of a high performance optimizer despite severe hardware limitations. The noise analysis component adaptively assigns the amount of fitness evaluations to be performed in order to distinguish two candidate solutions. In this way, it is assured that computational resources are not wasted and the selection of the most promising solution is correctly performed. The noise analysis employed in this algorithm spouses very well the pair-wise comparison logic typical of compact evolutionary algorithms. Numerical results show that the proposed algorithm significantly improves upon the performance, in noisy environments, of the standard compact genetic algorithm. Two implementation variants based on the elitist strategy have been tested in this studies. It is shown that the nonpersistent strategy is more robust to the noise than the persistent one and therefore its implementation seems to be advisable in noisy environments.