The derandomized evolution strategy (ES) with covariance matrix adaptation (CMA), is modified with the goal to speed up the algorithm in terms of needed number of generations. The idea of the modification of the algorithm is to adapt the covariance matrix in a faster way than in the original version by using a larger amount of the information contained in large populations. The original version...

Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the rst mutation{selection scheme to the reened (,){ES including the general concept of self{adaptation of the strategy parameters for the mutation variances as well as their ...

We revisit Gaussian Adaptation (GaA), a black-box optimizer for discrete and continuous problems that has been developed in the late 1960’s. This largely neglected search heuristic shares several interesting features with the wellknown Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and with Simulated Annealing (SA). GaA samples single candidate solutions from a multivariate normal dis...

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivativefree optimization algorithm. It optimizes a black-box objective function over a well defined parameter space. In some problems, such parameter spaces are defined using function approximation in which feature functions are manually defined. Therefore, the performance of those techniques strongly depends on the...

The well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space R. Recently, mirrored samples and sequential selection have been introduced within CMA-ES to improve its local search performances. In this paper, we benchmark the (1,4m)-CMA-ES which implements mirrored samples and sequent...

In this paper, three extensions of the BI-population Covariance Matrix Adaptation Evolution Strategy with weighted active covariance matrix update (BIPOP-aCMA-ES) are investigated. First, to address expensive optimization, we benchmark a recently proposed extension of the self-adaptive surrogate-assisted CMA-ES which benefits from more intensive surrogate model exploitation (BIPOP-saACM-k). Sec...

This paper proposes a competitive divide-and-conquer algorithm for solving large-scale black-box optimization problems, where there are thousands of decision variables, and the algebraic models of the problems are unavailable. We focus on problems that are partially additively separable, since this type of problem can be further decomposed into a number of smaller independent sub-problems. The ...

We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular we analyse Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation–Evolution Strategy (CMA-ES). Each evolutionary algorithms is contrasted with the others and with a robust non-stochastic gradient fol...

The Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space R. Recently, mirrored samples and sequential selection have been introduced within CMA-ES to improve its local search performances. In this paper, we benchmark the (1,4m)CMA-ES which implements mirrored samples and sequential selectio...

An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating realworld black-box fitness functions is sometimes very timeconsuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations. Therefore, surrogate regression models replacing the original expensi...