Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Genetic Search with Approximate Function Evaluation
Proceedings of the 1st International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Variances
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Optimal sampling and speed-up for genetic algorithms on the sampled OneMax problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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This paper investigates the utility of sampling as an evaluation-relaxation technique in genetic algorithms (GAs). In many real-world applications, sampling can be used to generate a less accurate, but computationally inexpensive fitness evaluator to speed GAs up. This paper focuses on the problem of polynomial regression as an example of problems with positive dependency among genes. Via statistical analysis of the noise introduced by sampling, this paper develops facet-wise models for the optimal sampling size, and these models are empirically verified. The results show that when the population is sized properly, small sampling sizes are preferred for most applications. When a fixed population size is adopted, which is usually the case in real-world applications, an optimal sampling size exists. If the sampling size is too small, the sampling noise increases, and GAs would perform poorly because of an insufficiently large population. If the sampling size is too large, the GA would spend too much time in fitness calculation and cannot perform well either within limited run duration.