Journal of Global Optimization
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
Computational Optimization and Applications
Genetic Algorithms in Noisy Environments
Machine Learning
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
A Nonparametric Approach to Noisy and Costly Optimization
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
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
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Particle filtering with particle swarm optimization in systems with multiplicative noise
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
Advances in Differential Evolution
Advances in Differential Evolution
Super-fit control adaptation in memetic differential evolution frameworks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on 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
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
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Generalised opposition-based differential evolution: an experimental study
International Journal of Computer Applications in Technology
Using Cartesian genetic programming to design wire antenna
International Journal of Computer Applications in Technology
Bio-inspired computation: success and challenges of IJBIC
International Journal of Bio-Inspired Computation
Hi-index | 0.00 |
This paper proposes a novel variant of differential evolution (DE) tailored to the optimisation of noisy fitness functions. The proposed algorithm, namely noise analysis differential evolution (NADE), combines the stochastic properties of a randomised scale factor and a statistically rigorous test which supports one-to-one spawning survivor selection that automatically selects a proper sample size and then selects, among parent and offspring, the most promising solution. The actions of these components are separately analysed and their combined effect on the algorithmic performance is studied by means of a set of numerous and various test functions perturbed by Gaussian noise. Various noise amplitudes are considered in the result section. The performance of the NADE has been extensively compared with a classical algorithm and two modern metaheuristics designed for optimisation in the presence of noise. Numerical results show that the proposed NADE has very good performance with most of the problems considered in the benchmark set. The NADE seems to be able to detect high quality solutions despite the noise and display high performance in terms of robustness.