# Search results for: Olivier Teytaud

Lecture Notes in Computer Science > Machine Learning and Knowledge Discovery in Databases > Regular Papers > 302-317

Lecture Notes in Computer Science > Artificial Evolution > Advances > 296-307

Lecture Notes in Electrical Engineering > Informatics in Control, Automation and Robotics > Intelligent Control Systems and Optimization > 95-106

*λ*-ES with standard step-size update-rules on a large family of fitness functions without any convexity assumption or quasi-convexity assumptions ([3,6]). The result provides a rule for choosing

*λ*and shows the consistency of halting criteria based on thresholds on the step-size. The family of functions under work is defined through a condition-number that generalizes...

Lecture Notes in Computer Science > Artificial Neural Networks — ICANN 2001 > Kernel Methods > 369-375

Lecture Notes in Computer Science > Machine Learning and Knowledge Discovery in Databases > Regular Papers > 293-305

Lecture Notes in Computer Science > Genetic Programming > Posters > 268-277

Lecture Notes in Computer Science > Applications of Evolutionary Computation > EvoSTOC Contributions > 592-601

*same*algorithm is able to adapt to several frameworks, including some for which no bound has never been derived. Incidentally, bounds derived by [16] for noise quickly decreasing to zero around the optimum are extended to the more general case of a positively lower-bounded...

Lecture Notes in Computer Science > Genetic Programming > Posters > 327-338

*O*(1/

*d*) for the constant in the linear convergence (i.e. the constant...

Lecture Notes in Computer Science > Learning and Intelligent Optimization > Main Track (Regular Papers) > 97-110

Lecture Notes in Computer Science > Learning and Intelligent Optimization > Main Track (Regular Papers) > 111-124

Lecture Notes in Computer Science > Applications of Evolutionary Computing > EvoNUM Contributions > 655-664

*λ*large. The rule we use, essentially based on estimation of multivariate normal algorithm, is (i) compliant with all families of distributions for which a density estimation algorithm exists (ii) simple (iii) parameter-free (iv) better than current rules in this framework of

*λ*large. The speed-up as a function...

Lecture Notes in Computer Science > Learning and Intelligent Optimization > Main Track (Regular Papers) > 433-445