Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
Integrating techniques from statistical ranking into evolutionary algorithms
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
Neuroevolutionary Inventory Control in Multi-Echelon Systems
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
| Hi-index | 0.00 |
Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested techniques.