0045-7949/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.compstruc.2010.09.001 ⇑ Corresponding author. E-mail addresses: [email protected] (M Colorado.edu (O.V. Vasilyev), [email protected] (P. Koum We present results from the shape optimization of linked bodies for drag reduction in simulations of incompressible flow at moderate Reynolds numbers. The optimization relies on the cov...

Optimum implementation of non-conventional wells allows us to increase considerably hydrocarbon recovery. By considering the high drilling cost and the potential improvement in well productivity, well placement decision is an important issue in field development. Considering complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable appro...

In this paper, we investigate the performance of CMA-ES on large scale non-separable optimisation problems. CMA-ES is a robust local optimiser that has shown great performance on small-scale nonseparable optimisation problems. Self-adaptation of a covariance matrix makes it rotational invariant which is a desirable property, especially for solving non-separable problems. The focus of this paper...

Hyperparameters of deep neural networks are often optimized by grid search, random search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is known for its state-of-the-art performance in derivative-free optimization. CMA-ES has some useful invariance properties and is friendly to parallel evaluations of solutions...

We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms seek to optimize a neural network which provides the policy for playing a simple game (TicTacToe). Our contribution is to study the effect of varying learning conditions on learning speed and quality. Certain initial fai...

Optimal parameter model finding is usually a crucial task in engineering applications of classification and modelling. The exponential cost of linear search on a parameter grid of a given precision rules it out in all but the simplest problems and random algorithms such as uniform design or the covariance matrix adaptation-evolution strategy (CMA-ES) are usually applied. In this work we shall p...

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective function needed to approximate the global optimum. This weakness is commonly addressed through surrogate optimization, learning an estimate of t...

In this paper, we propose an adaptation of evolutionary strategies (ES) for forecasting the exchange rate. The proposed method employed the evolution of the functional form as well as its coefficients. Using mutation, the functional form is evolved from an initial population. Evolution strategies is used to search for coefficients of functions. We used the data of bath/us-dollar exchange rate f...

The present paper discusses the relationship between evolutionary computation (EC) and further benefit of a kind of inconvenience (FUBEN-EKI), which is a research project to explore methods by which to design artifacts while appreciating the benefits of inconvenience. Evolutionary computation contributes to FUBEN-EKI both theoretically and practically. The design theory for implementing the ben...