The Covariance Matrix Adaptation Evolution Strategy (CMAES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called se...

This paper presents a lens system design algorithm using the covariance matrix adaptation evolution strategy (CMA-ES), which is one of the most powerful self-adaptation mechanisms. The lens design problem is a very difficult optimization problem because the typical search space is a complicated multidimensional space including many local optima, non-linearities, and strongly correlated paramete...

In this paper, a hardware implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is presented. This algorithm is based on the adaptation of the covariance matrix, initially it is focused on a region of the particular search space and subsequently, it moves or grows along the search space, as appropriate to find the optimum value. The experimental results reveal...

Added credits to the s∗ACM-ES algorithm. Section 1 Added references and clarified the motivation. Section 3 Added references. Abstract: The interest in accelerating black-box optimizers has resulted in several surrogate model-assisted version of the Covariance Matrix Adaptation Evolution Strategy, a state-of-the-art continuous black-box optimizer. The version called Surrogate CMA-ES uses Gaussi...

The Covariance matrix adaptation evolution strategy (CMA-ES) evolves a multivariate Gaussian distribution for continuous optimization. The evolution path, which accumulates historical search direction in successive generations, plays a crucial role in the adaptation of covariance matrix. In this paper, we investigate what the evolution path approximates in the optimization procedure. We show th...

The covariance matrix adaptation (CMA) is one of the most powerful self adaptation mechanisms for Evolution Strategies. However, for increasing search space dimension N , the performance declines, since the CMA has space and time complexity O(N2). Adapting the main mutation vector instead of the covariance matrix yields an adaptation mechanism with space and time complexity O(N). Thus, the main...

Abstract: An improved covariance matrix adaptation evolution strategy algorithm (CMA-ES) is proposed and it is used to train the forecasting model of the network security situation in this paper. A new recombination strategy which adds a heuristic component is developed in the improved CMA-ES algorithm, and the search speed is accelerated. The experimental results show that, compare with origin...

The covariance matrix adaptation evolution strategy (CMAES) is suggested for solving problems described by Markov decision processes. The algorithm is compared with a state-of-the-art policy gradient method and stochastic search on the double cart-pole balancing task using linear policies. The CMA-ES proves to be much more robust than the gradient-based approach in this scenario.

— In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the...

— In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the...