Prof. Dr. rer. nat. habil. Hans-Georg Beyer

Recent Talks and Tutorials

1. Evolution Strategies are Not Gradient Followers

In order to explain the working principles of Evolution Strategies (ESs) in real-valued search spaces, sometimes the picture of a (stochastic) gradient approximating strategy is invoked. There are publications in the field of machine learning and evolutionary algorithms where this misleading picture is promoted. Therefore, I gave a talk at Dagstuhl, Seminar 19431 (Oct. 20 - 25, 2019), showing that this picture is not correct: ESs are much more explorative than gradient strategies, thus they have a certain chance of not being trapped in the next local attractor. The slides of that talk can be obtained here.
BTW, even the consideration of ESs as Monte-Carlo approximators of the so-called natural gradient does not hold for standard ESs such as the Covariance Matrix Adapation ES. A discussion of that topic can be found in my paper Convergence Analysis of Evolutionary Algorithms That are Based on the Paradigm of Information Geometry. While the main part of that paper is rather technical, the Introduction and the Conclusions should be easy to follow.

2. Design Principles for Matrix Adaptation Evolution Strategies

In the paper Simplify Your Covariance Matrix Adaptation Evolution Strategy we have shown that one can simplify this well-performing ES by removing the covariance matrix totally from the CMA-ES without performance degradation. As a result one obtains simpler Evolution Strategies that allow for further algorithm engineering addressing high-dimensional search spaces and constrained optimization problems. Here are tutorial slides discussing these topics.
Matlab/Octave code of the basic algorithms can be found at Downloads

last change: 14.07.2020