Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
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 Noisy Environments
Machine Learning
Evolutionary Signal Enhancement Based on Hölder Regularity Analysis
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Optimization of Noisy Fitness Functions by Means of Genetic Algorithms Using History of Search
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th 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
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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When considering noisy fitness functions for some CPU-time consuming applications, a trade-off problem arise: how to reduce the influence of the noise while not increasing too much computation time. In this paper, we propose and experiment some new strategies based on an exploitation of historical information on the algorithm evolution, and a non-generational evolutionary algorithm.