Genetic Algorithms in Noisy Environments
Machine Learning
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
Particle filtering with particle swarm optimization in systems with multiplicative noise
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An improved differential evolution scheme for noisy optimization problems
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Prudent-Daring vs tolerant survivor selection schemes in control design of electric drives
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
IEEE Transactions on Evolutionary Computation
A study on scale factor in distributed differential evolution
Information Sciences: an International Journal
Random lines: a novel population set-based evolutionary global optimization algorithm
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Noise analysis compact genetic algorithm
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Fast mixed strategy differential evolution using effective mutant vector pool
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A study on scale factor/crossover interaction in distributed differential evolution
Artificial Intelligence Review
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This paper proposes a Differential Evolution based algorithm for numerical optimization in the presence of noise. The proposed algorithm, namely Noise Analysis Differential Evolution (NADE), employs a randomized scale factor in order to overcome the structural difficulties of a Differential Evolution in a noisy environment as well as a noise analysis component which determines the amount of samples required for characterizing the stochastic process and thus efficiently performing pairwise comparisons between parent and offspring solutions. The NADE has been compared, for a benchmark set composed of various fitness landscapes under several levels of noise bandwidth, with a classical evolutionary algorithm for noisy optimization and two recently proposed metaheuristics. Numerical results show that the proposed NADE has a very good performance in detecting high quality solutions despite the presence of noise. The NADE seems, in most cases, very fast and reliable in detecting promising search directions and continuing evolution towards the optimum.