Prof. Dr. rer. nat. habil. Hans-Georg Beyer
1. Evolution Strategy Examples (for teaching only)
For the underlying algorithmic ideas refer to Scholarpedia's Evolution
2. Matrix Adaptation Evolution Strategies (MA-ES)
Fast MA-ES with O(N^2) algorithmic complexity
A tar-ball containing Matlab/Octave code that avoids matrix-matrix-
multiplication (see Slide 32 in my tutorial Design
Principles for Matrix Adaptation Evolution Strategies).
Experiments can be started in Experiment.m
Meta MA-ES aka Bi-Population MA-ES (BiPop-MA-ES)
In order to optimize highly multi-modal optimization problems, the
population size must be learned on-the-fly. This can be done by
running two MA-ES with restarts (for details see Simplify
Your Covariance Matrix Adaptation Evolution Strategy). A
tar-ball containing Matlab/Octave code is provided below.
Experiments can be started in BiPop_MAES_test.m
Limited Memory MA-ES (LM-MA-ES)
The matrix-vector operations are the algorithmic bottleneck in the
fast MA-ES. If one wants to reduce the algorithmic complexity below
O(N^2) one must approximate these operations. It turns out that one
can reduce this complexity to O(N log N) by introducing a set of
path cumulation vectors at different time scales to approximate the
matrix vector operations for generation of mutations. The resulting
LM-MA-ES has been published in Large
Scale Black-Box Optimization by Limited-Memory Matrix Adaptation).
This algorithm is able to handle 10000 variables (if efficiently
coded in C or Fortran). A tar-ball containing Matlab/Octave code
(suited for somewhat smaller problem sizes) is provided below.
Experiments can be started in Test_LM_MA_ES.m
3. Lecture Slides (password protected)
TLV FZ PPE
last change: 14.07.2020