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...

The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences , life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to var...

Purpose: Against a backdrop of continued weak economic conditions and with many firms experiencing declining financial performance, this paper presents a synthesis of the business turnaround literature. It aims to identify which turnaround and recovery strategies have been effective historically, based on the evidence provided by previous empirical research. Design/methodology/approach: We revi...

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...

Evolution Strategies (ES) have recently been demonstrated to be a viable alternative to reinforcement learning (RL) algorithms on a set of challenging deep RL problems, including Atari games and MuJoCo humanoid locomotion benchmarks. While the ES algorithms in that work belonged to the specialized class of natural evolution strategies (which resemble approximate gradient RL algorithms, such as ...

3 Adapting the Covariance Matrix 10 3.1 Estimating the Covariance Matrix From Scratch . . . . . . . . . . . . . . . . 10 3.2 Rank-μ-Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3 Rank-One-Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3.1 A Different Viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3.2 Cumulation: Uti...