In this paper we show how to modify a large class of evolution strategies (ES’s) for unconstrained optimization to rigorously achieve a form of global convergence, meaning convergence to stationary points independently of the starting point. The type of ES under consideration recombines the parent points by means of a weighted sum, around which the offspring points are computed by random genera...

This paper investigates the behavior of PSO (particle swarm optimization) and CMA-ES (covariance matrix adaptation evolution strategy) on ill-conditioned functions. The paper also highlights momentum as important common concept used in both algorithms and reviews important invariance properties. On separable, ill-conditioned functions, PSO performs very well and outperforms CMA-ES by a factor o...

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (S...

In the field of scientific modeling, one is often confronted with the task of drawing samples from a probability distribution that is only known up to a normalizing constant and for which no direct analytical method for sample generation is available. Since the past decade, adaptive Markov Chain Monte Carlo (MCMC) methods gained considerable attention in the statistics community in order to tac...

A common shortcoming in the Evolutionary Computation (EC) community is that the publication of many search heuristics is not accompanied by rigorous benchmarks on a balanced set of test problems. A welcome effort to promote such test suites are the IEEE CEC competitions on real-valued black-box optimization. These competitions prescribe carefully designed synthetic test functions and benchmarki...

The paper considers the extension of CMA-ES algorithm using mixtures distributions for finding optimal hyperparameters neural networks. Hyperparameter optimization, formulated as optimization black box objective function, which is a necessary condition automation and high performance machine learning approaches. an efficient without derivatives, one alternatives in combination hyperparameter me...

Climatic change and stress is a major driving force of evolution. The effects of climate change on living organisms have been shown primarily on regional and global scales. Here I propose the "Evolution Canyon" (EC) microscale model as a potential life monitor of global warming in Israel and the rest of the world. The EC model reveals evolution in action at a microscale involving biodiversity d...

This paper analyzes the Self-Adaptation (SA) algorithm widely used to adapt strategy parameters of the Evolution Strategy (ES) in order to obtain maximal ES-performance. The investigations are concentrated on the adaptation of one general mutation strength (called SA) in (1;) ESs. The hypersphere serves as the tness model. Starting from an introduction into the basic concept of self-adaptation,...