Evolution strategies are one of the most successful classes of stochastic optimization algorithms for solving real world problems, which involves discontinuous, discrete or mixed-integer search space with nonlinear constraints. Since the creation of evolution strategies back in the 1960s, a variety of improvements and modifications have been suggested to enhance its performance. The covariance ...

MiniZinc is a solver-independent constraint modeling language which is increasingly used in the constraint programming community. It can be used to compare different solvers which are currently based on either constraint programming, Boolean satisfiability or mixed integer linear programming. In this paper we show how MiniZinc models can be compiled into fitness functions for evolutionary algor...

This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to Gaussian CMA-ES, proposed generates mutation vectors from a mixture model, which facilitates exploiting rich variable correlations of problem landscape within limited time budget. We analyze probability distribution this model and show that it ...

The paper presents the asymptotical analysis of a technique for improving the convergence of evolution strategies (ES) on noisy t-ness data. This technique that may be called \Mutate large, but inherit small", is discussed in light of the EPP (evolutionary progress principle). The derivation of the progress rate formula is sketched, its predictions are compared with experiments, and its limitat...

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...

The NMR spectrum of n-hexane orientationally ordered in the nematic liquid crystal ZLI-1132 is analysed using covariance matrix adaptation evolution strategy (CMA-ES). The spectrum contains over 150 000 transitions, with many sharp features appearing above a broad, underlying background signal that results from the plethora of overlapping transitions from the n-hexane as well as from the liquid...

Sample efficiency is a critical property when optimizing policy parameters for the controller of a robot. In this paper, we evaluate two state-of-the-art policy optimization algorithms. One is a recent deep reinforcement learning method based on an actor-critic algorithm, Deep Deterministic Policy Gradient (DDPG), that has been shown to perform well on various control benchmarks. The other one ...

An appropriate preprocessing of EEG signals is crucial to get high classification accuracy for Brain–Computer Interfaces (BCI). The raw EEG data are continuous signals in the timedomain that can be transformed by means of filters. Among them, spatial filters and selecting the most appropriate frequency-bands in the frequency domain are known to improve classification accuracy. However, because ...

In combinatorial solution spaces Iterated Local Search (ILS) turns out to be exceptionally successful. The question arises: is ILS also capable of improving the optimization process in continuous solution spaces? To demonstrate that hybridization leads to powerful techniques in continuous domains, we introduce a hybrid meta-heuristic that integrates Powell’s direct search method. It combines di...

This paper presents The following Applications of Optimal adaptation hypothesis to solve some of the standard problems of optimal adaptation This method random select some nodes in the interval of integration in the beginning. The number of object parameters as well as the number of associated Evolution Strategies parameters can be adapted during the Optimal adaptation. This new method presents...