TY - RPRT
A1 - Bartz-Beielstein, Thomas
A1 - Branke, Jürgen
A1 - Mehnen, Jörn
A1 - Mersmann, Olaf
T1 - Overview: Evolutionary Algorithms
N2 - Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications
T3 - CIplus - 2/2015
KW - Soft Computing
KW - Versuchsplanung
KW - Evolutionsstrategie
KW - Evolutionärer Algorithmus
KW - Metaheuristik
KW - Optimierung
KW - Optimierungsproblem
KW - Evolutionäre Algorithmen
KW - Evolutionsstrategien
KW - Genetisches Programmieren
KW - Genetische Algorithmen
KW - Evolutionary Algorithms
KW - Evolution Strategies
KW - Genetic Algorithms
KW - Genetic programming
Y1 - 2015
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-777
ER -
TY - RPRT
A1 - Bartz-Beielstein, Thomas
A1 - Jung, Christian
A1 - Zafferer, Martin
T1 - Sequential Parameter Optimization in Noisy Environments
N2 - Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.
T3 - CIplus - 4/2015
KW - Evolutionärer Algorithmus
KW - Metaheuristik
KW - Optimierung
KW - Optimierungsproblem
KW - Unsicherheit
KW - Kriging
KW - Co-Kriging
KW - Metamodel
KW - Kriging
KW - Co-Kriging
KW - Metamodel
Y1 - 2015
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-841
ER -