TY - RPRT
A1 - Bartz-Beielstein, Thomas
A1 - Gentile, Lorenzo
A1 - Zaefferer, Martin
T1 - In a Nutshell: Sequential Parameter Optimization
N2 - The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using
a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the Sequential Parameter Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate
how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.
T3 - CIplus - 7/2017
KW - Algorithm Tuning
KW - Optimization
KW - Surrogate Models
KW - SPOT
KW - R
Y1 - 2017
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-5928
ER -
TY - RPRT
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Naujoks, Boris
A1 - Bartz-Beielstein, Thomas
T1 - Simulation-based Test Functions for Optimization Algorithms
N2 - When designing or developing optimization algorithms, test functions are crucial to evaluate
performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world
applications. Previously,
test functions with real-world relevance were generated by training a machine learning model based on
real-world data. The model estimation is used as a test function.
We propose a more principled approach using simulation instead of estimation.
Thus, relevant and varied test functions
are created which represent the behavior of real-world fitness landscapes.
Importantly, estimation can lead to excessively smooth test functions
while simulation may avoid this pitfall. Moreover, the simulation
can be conditioned by the data, so that the simulation reproduces the training data
but features diverse behavior in unobserved regions of the search space.
The proposed test function generator is illustrated with an intuitive, one-dimensional
example. To demonstrate the utility of this approach it
is applied to a protein sequence optimization problem.
This application demonstrates the advantages as well as practical limits of simulation-based
test functions.
T3 - CIplus - 3/2017
KW - Optimization
KW - Test function generator
KW - Simulation
KW - Modeling
Y1 - 2017
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4777
ER -