Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The ARGOT strategy: adaptive representation genetic optimizer technique
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Genetic Algorithms in Noisy Environments
Machine Learning
Genetic Search with Approximate Function Evaluation
Proceedings of the 1st International Conference on Genetic Algorithms
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
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In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling.