In this article we present an automatic method for charge and mass identification of charged nuclear fragments produced in heavy ion collisions at intermediate energies. The algorithm combines a generative model of DeltaE - E relation and a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The CMA-ES is a stochastic and derivative-free method employed to search parameter space of the...

|A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the tness function (or the coordinate system). Especially rotation invariance, here resulting directly from the ...

A hybrid approach that combines the (1+1)-ES and threshold selection methods is developed. The framework of the new experimentalism is used to perform a detailed statistical analysis of the effects that are caused by this hybridization. Experimental results on the sphere function indicate that hybridization worsens the performance of the evolution strategy, because evolution strategies are well...

Taking inspiration from approximate ranking, this paper investigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. Whereas the choice of the SVM kernel is known to be a critical issue, the proposed approach uses the Covariance Matrix adapted by CMA-ES wit...

Based on evolutionary computation techniques, we present a parallel, globally convergent, multiobjective optimization algorithm which extends the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). This approach enables identifying multiple global optima and multiple discontinuous Pareto set solutions of the optimization problem in a compact search space. After evaluating the algorithm...

Information geometric optimization (IGO) is a general framework for stochastic optimization problems aiming at limiting the influence of arbitrary parametrization choices: the initial problem is transformed into the optimization of a smooth function on a Riemannian manifold, defining a parametrization-invariant first order differential equation and, thus, yielding an approximately parametrizati...

In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations required increase restarts for better local optimum solutions, propose new approach referred to as adaptive subset selection (AS3). The proposed subsamples support construct objective function in given neighborhood, introduc...

Multiphoton microscopy is the enabling tool for biomedical research, but aberrations of biological tissues have limited its imaging performance. Adaptive optics (AO) has been developed to partially overcome aberration restore For indirect AO, algorithm key successful implementation. Here, based on fact that AO an analogy black-box optimization problem, we successfully apply covariance matrix ad...