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 - Breiderhoff, Beate
A1 - Bartz-Beielstein, Thomas
A1 - Naujoks, Boris
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Flasch, Oliver
A1 - Friese, Martina
A1 - Mersmann, Olaf
A1 - Stork, Jörg
T1 - Simulation and Optimization of Cyclone Dust Separators
N2 - Cyclone Dust Separators are devices often used to filter solid particles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a minimal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise variables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective optimization algorithm. For the harder, stochastic optimization problem, a surrogate-model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget.
T3 - CIplus - 4/2013
KW - Soft Computing
KW - Evolutionärer Algorithmus
KW - Mehrkriterielle Optimierung
KW - Entstauber
KW - Simulation
KW - Mehrkriterielle Optimierung
KW - Surrogat-Modellierung
KW - Sequentielle Parameter Optimierung
KW - Zylon Enstauber
KW - Multiobjective Optimization
KW - Multi-Criteria Optimization
KW - Surrogate Modeling
KW - Sequential Parameter Optimization
KW - Cyclone Dust Separator
Y1 - 2013
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-470
ER -