| Berny, A. : Statistical Machine Learning and Combinatorial Optimization. In Kallel, L., Naudts, B. and Rogers, A., editors, Theoretical Aspects of Evolutionary Computing, Springer (2001). |
....mutation, since we consider that so called correlated mutations stem from the application of selection to an implicit Gaussian density. We can however reintroduce mutation in a more classical way in the form of noise added to parameters, in the spirit of stochastic algorithms. In a previous paper [Ber00], we have introduced a Gaussian density in the context of optimization in dis crete spaces. We have used a decomposition of the covariance matrix which was not satisfactory and led to numerical instabilities. In the present paper, we use a different decomposition which separates orientation and ....
.... t 1 = t N X i=1 f(x i ) P N j=1 f(x j ) grad log p(x i ) where 0 1 is the learning rate. With such an approximation scheme, updating complexity is Nn n 2 for m and Nn for . 4. 2 Reinforcement Learning This is the numerical approximation which has been used in [Ber00] in the case of optimization over the space of fixed length binary strings. It is close to classical reinforcement learning rules such as those appearing in [Wil92] and also to stochastic gradient techniques [Duf96] We first recall that grad p(x) grad log p(x) p(x) which we have already ....
A. Berny. Statistical machine learning and combinatorial optimization. In L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical Aspects of Evolutionary Computing, Lecture Notes in Natural Computing. Springer-Verlag, 2000.
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Berny, A. : Statistical Machine Learning and Combinatorial Optimization. In Kallel, L., Naudts, B. and Rogers, A., editors, Theoretical Aspects of Evolutionary Computing, Springer (2001).
No context found.
A. Berny, Statistical machine learning and combinatorial optimization, Theoretical Aspects of Evolutionary Computing (L. Kallel, B. Naudts, and A. Rogers, eds.), Springer, 2001.
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