Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
An empirical evaluation of several methods to select the best system
ACM Transactions on Modeling and Computer Simulation (TOMACS)
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Selecting a Selection Procedure
Management Science
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Heuristics for sampling repetitions in noisy landscapes with fitness caching
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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In evolutionary algorithms, the typical post-processing phase involves selection of the best-of-run individual, which becomes the final outcome of the evolutionary run. Trivial for deterministic problems, this task can get computationally demanding in noisy environments. A typical naive procedure used in practice is to repeat the evaluation of each individual for the fixed number of times and select the one with the highest average. In this paper, we consider several algorithms that can adaptively choose individuals to evaluate basing on the results evaluations which have already been performed. The procedures are designed without any specific assumption about noise distribution. In the experimental part, we compare our algorithms with the naive and optimal procedures, and find out that the performance of typically used naive algorithm is poor even for relatively moderate noise. We also show that one of our algorithms is nearly optimal for most of the examined situations.