| Research
Center Business Informatics |
| Hans-Georg
Beyer |
 Curriculum
Vitae |
| Publications
and Patents |
| Downloads |
| Information
Sources |
| Evolutionary
Algorithms Terms and Definitions |
| Evolutionäre
Algorithmen Begriffe, Definitionen und Wörterbuch |
Prof. Dr. rer. nat. habil. Hans-Georg Beyer |
Downloads
1. Evolution Strategy Examples (for teaching only)
For the underlying algorithmic ideas refer to Scholarpedia's Evolution Strategies.Mathematica Sources
Matlab/Octave Sources
- simple
self-adaptive
(mu/mu_I, lambda)-sigma-SA-ES (Octave)
- simple Covariance Matrix Adaptation ES (CMA-ES) (Octave)
- simple CMA-ES for matlab "cma-es.m" and corresponding function definitions "CMArecomb.m", "SortPop.m", and "fitness.m"
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
- Natural
Problem Solving: Computational Intelligence (CI)
- Evolutionary
Algorithms
(EAs): A Short Introduction
last change: 14.07.2020