This paper describes a new approach to optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the method evolves a population of functions. The purpose of such functions is to transform initial random values of the parameters into better ones. The representation is, in principle, independent of the size of the problem being addressed. Promising results are reported, comparing the new method with differential evolution, particle swarm optimization, and genetic algorithms, on a test suite of benchmark problems.
This paper introduces a numerical model to estimate fatigue life under step‐stress conditions, using the Weibull and lognormal distributions. The maximum likelihood method was used to estimate the free parameters of the distributions. The model was fitted to an experimental data on fatigue life in the specimens of steel SAE 8620, by using evolutionary computation to optimize the likelihood function. Results are reported on the values of the parameters and their confidence interval. Also, a validation of the model is discussed using analysis of residuals.
Abstract. This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new method evolves functions that transform initial random values for the parameters into optimal ones. This new representation allows the incorporation of knowledge about the problem being solved to improve the search. Moreover, the new approach addresses the scalability problem by using a representation that, in principle, is independent of the size of the problem being addressed. Promising results are reported, comparing the new method with differential evolution and particle swarm optimization on a test suite of benchmark problems.
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