Genetic Algorithms in Noisy Environments
Machine Learning
Fitness landscapes and evolvability
Evolutionary Computation
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
Searching in the Presence of Noise
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On selecting the best individual in noisy environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Finding forms of flocking: evolutionary search in ABM parameter-spaces
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Finding forms of flocking: evolutionary search in ABM parameter-spaces
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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For many large-scale combinatorial search/optimization problems, meta-heuristic algorithms face noisy objective functions, coupled with computationally expensive evaluation times. In this work, we consider the interaction between the technique of "fitness caching" and the straightforward noise reduction approach of "fitness averaging" by repeated sampling. Fitness caching changes how noise affects a fitness landscapes, as noisy values become frozen in the cache. Assuming the use of fitness caching, we seek to develop heuristic methods for predicting the optimal number of sampling replications for fitness averaging. We derive two analytic measures for quantifying the effects of noise on a cached fitness landscape (probabilities of creating "false switches" and "false optima"). We empirically confirm that these measures correlate well with observed probabilities on a set of four well-known test-bed functions (sphere, Rosenbrock, Rastrigin, Schwefel). We also present results from a preliminary experimental study on these landscapes, investigating four possible heuristic approaches for predicting the optimal sampling, using a random-mutation hill-climber with fitness caching.