mike07.bib
@incollection{Preuss2007,
author = {Mike Preu\ss{} and Thomas Bartz--Beielstein},
title = {Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded
Evolutionary Algorithms},
booktitle = {Parameter Setting in Evolutionary Algorithms},
publisher = {Springer},
year = 2007,
editor = {Fernando Lobo and Claudio Lima and Zbigniew Michalewicz},
series = {Studies in Computational Intelligence},
pages = {91--120},
address = {Berlin,\ Heidelberg,\ New\ York}
}
@inproceedings{Rudolph2007,
author = {G{\"u}nter Rudolph and
Boris Naujoks and
Mike Preuss},
editor = {Shigeru Obayashi and
Kalyanmoy Deb and
Carlo Poloni and
Tomoyuki Hiroyasu and
Tadahiko Murata},
title = {Capabilities of EMOA to Detect and Preserve Equivalent Pareto
Subsets},
booktitle = {Evolutionary Multi-Criterion Optimization, 4th International
Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007,
Proceedings},
pages = {36--50},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {4403},
year = {2007},
isbn = {3-540-70927-4},
ee = {http://dx.doi.org/10.1007/978-3-540-70928-2_7},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@inproceedings{Preuss2007c,
title = {Solving Multimodal Problems via Multiobjective
Technique with Application to Phase Equilibrium
Detection},
author = {Mike Preuss and G{\"u}nter Rudolph and Feelly
Tumakaka},
pages = {2703--2710},
booktitle = {2007 IEEE Congress on Evolutionary Computation},
year = {2007},
editor = {Dipti Srinivasan and Lipo Wang},
address = {Singapore},
organization = {IEEE Computational Intelligence Society},
publisher = {IEEE Press},
isbn = {1-4244-1340-0},
abstract = {For solving multimodel problems by means of
evoluationary algorithms, one often resorts or niching
methods. The latter approach the question: 'What is
else-where?' by an implicit second criterion in order
to keep population distributed over the search space
Induced by a practical problem that appears to be
simple but is not easily solved, a multiobjective
algorithm is proposed for solving multimodel problems.
It employs an explicit diversity criterion as second
objective. Experimental comparison with standard
methods suggests that the multiobjective algorithm is
fast and relaible and that coupling it with a local
search technique is straightforward and leads to
enormous quality valuable gain. The combined algorithm
is still fast and may be especially valuable for
practical problems with costly target funtion
evaluations.},
notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET. IEEE Catalog Number: 07TH8963C}
}
@inproceedings{Stoean:2007d,
title = {Concerning the Potential of Evolutionary Support
Vector Machines},
author = {Ruxandra Stoean and Mike Preuss and Catalin Stoean and
D. Dumitrescu},
pages = {1436--1443},
booktitle = {2007 IEEE Congress on Evolutionary Computation},
year = {2007},
editor = {Dipti Srinivasan and Lipo Wang},
address = {Singapore},
organization = {IEEE Computational Intelligence Society},
publisher = {IEEE Press},
isbn = {1-4244-1340-0},
abstract = {Within the present paper, we put forward a novel
hybridization between support vector machines and
evolutionary algorithms. Evolutionary support vector
machines consider the classification task as in support
vector machines but use an evolutionary algorithm to
solve the optimization problem of determining the
decision function. They can explicitly acquire the
coefficients of the separating hyperplane, which is
often not possible within the classical technique. More
important, evolutionary support vector machines obtain
the coefficients directly from the evolutionary
algorithm and can refer them at any point during a run.
In addition, they do not require properties of positive
(semi-)definition for kernels within nonlinear
learning. The concept can be furthermore extended to
handle large amounts of data, a problem frequently
occurring e.g. in spam mail detection, one of our test
cases. An adapted chunking technique is therefore
alternatively used. In addition to two different
representations, a crowding variant of the evolutionary
algorithm is tested in order to investigate whether the
performance of the algorithm is maintained; its global
search capabilities would be important for the
prospected coevolution of non-standard kernels.
Evolutionary support vector machines are validated on
four real-world classification tasks; obtained results
show the promise of this new approach.},
notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET. IEEE Catalog Number: 07TH8963C}
}
@inproceedings{Stoean2007c,
author = {Catalin Stoean and
Mike Preuss and
Ruxandra Stoean and
Dumitru Dumitrescu},
title = {Disburdening the species conservation evolutionary algorithm
of arguing with radii},
editor = {Hod Lipson},
booktitle = {Genetic and Evolutionary Computation Conference, GECCO 2007,
Proceedings, London, England, UK, July 7-11, 2007},
publisher = {ACM},
year = {2007},
pages = {1420--1427},
bibsource = {DBLP, http://dblp.uni-trier.de},
doi = {http://doi.acm.org/10.1145/1276958.1277220},
isbn = {978-1-59593-697-4}
}
@inproceedings{Chimani2007,
author = {Markus Chimani and
Maria Kandyba and
Mike Preuss},
editor = {Thomas Bartz-Beielstein and
Mar\'{\i}a J. Blesa Aguilera and
Christian Blum and
Boris Naujoks and
Andrea Roli and
G{\"u}nter Rudolph and
Michael Sampels},
title = {Hybrid Numerical Optimization for Combinatorial Network
Problems},
booktitle = {Hybrid Metaheuristics, 4th International Workshop, HM 2007,
Dortmund, Germany, October 8-9, 2007, Proceedings},
year = {2007},
pages = {185--200},
doi = {http://dx.doi.org/10.1007/978-3-540-75514-2_14},
bibsource = {DBLP, http://dblp.uni-trier.de},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {4771},
isbn = {978-3-540-75513-5}
}
@article{Henrich2007,
author = {Henrich, F. and Bouvy, C. and Kausch, C. and Lucas, K. and Preuss, M. and Rudolph, G. and Roosen, P.},
title = {Economic optimization of non-sharp separation sequences by means of evolutionary algorithms},
journal = {Computers chem. Engng.},
volume = {32},
number = {7},
year = {2007},
pages = {1411-1432}
}
@article{StoeanActaCib,
author = {R. Stoean and C. Stoean and M. Preuss and D. Dumitrescu},
title = {Evolutionary Detection of Separating Hyperplanes in E-mail Classification},
journal = {Acta Cibiniensis},
year = {2007},
volume = {LV},
pages = {41--46}
}
@inproceedings{sto07c,
author = {C. Stoean and R. Stoean and M. Preuss and D. Dumitrescu},
title = {Competitive Coevolution for Classification},
booktitle = {7th International Conference on Artificial Intelligence and Digital Communications (AIDC 2007)},
year = {2007},
pages = {28-39},
publisher = {Research Notes in Artificial Intelligence and Digital Communications, N. Tandareanu (Ed.), Reprograph Press},
address = {Craiova, Romania}
}