This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to...

This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMAES) to the Generation Expansion Planning (GEP) problem. RCMAES is a class of continuous Evolutionary Algorithm (EA) derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The...

In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of...

Global optimisation of unknown noisy functions is a daunting task that seems to appear in domains ranging from games to control problems to meta-parameter optimisation for machine learning. We show how to incorporate heuristics to Stochastic Simultaneous Optimistic Optimization (STOSOO), a global optimisation algorithm that has very weak requirements from the function. In our case, heuristics c...

Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (GED). In this paper, data transformations are optimized instead. This is equivalent to searching for GEDs, but can be applied to any learning algorithm, even if it does not use distances explicitly. Two optimization t...

Proportional integral derivative (PID) controllers are important and widely used tools of system control. In this paper, we deal with the problem of tuning multiple coupled PID controllers within the practical context of combustion engine simulations, where no information about the controlled system is provided. We formulate the problem as a black-box optimization problem and, based on its prop...

This paper presents a performance analysis of the recently proposed σ-self-adaptive weighted recombination evolution strategy (ES) with scaled weights. The steady state behavior of this ES is investigated for the non-noisy and noisy case, and formulas for the optimal choice of the learning parameter are derived allowing the strategy to reach maximal performance. A comparison between weighted mu...

Cumulative step-size adaptation (CSA) based on path length control is regarded as a robust alternative to the standard mutative self-adaptation technique in evolution strategies (ES), guaranteeing an almost optimal control of the mutation operator. This paper shows that the underlying basic assumption in CSA--the perpendicularity of expected consecutive steps--does not necessarily guarantee opt...

Till now, only uncorrelated evolution strategies benefit from the strength of the directed mutation principle. It is thus a natural idea to enhance more powerful correlated evolution strategies with directed mutation, too. This work aims at porting this approach from the uncorrelated setting in classical evolution strategies to the correlated case as given in covariance matrix adaptation-evolut...

This paper presents first results of an analysis of the σ-self-adaptation mechanism on the sharp ridge for non-recombinative (1, λ) evolution strategies (ES). To analyze the ES’s evolution, we consider the so-called evolution equations which describe the one-generation change. Neglecting stochastic perturbations and considering only the mean value dynamics, we will investigate possible causes w...