SPOTLIGHT
My TED talk "Tech Kangaroos: Evolution at Work"
TEDx Danubia 2011, watch it
here
The first comprehensive
text book on evolutionary computing:
A.E. Eiben and J.E. Smith,
Introduction to Evolutionary
Computing, Springer,
First edition, 2003, ISBN 3-540-40184-9, Corrected 2nd printing, 2007,
ISBN:
978-3-540-40184-1
The award winning paper
on parameter control:
A.E. Eiben, R. Hinterding, and Z. Michalewicz,
Parameter
control in evolutionary algorithms, IEEE Transactions of EC,
3(2):124-141, 1999
The grand
overview paper on parameter tuning:
A.E. Eiben and S.K. Smit,
Parameter
Tuning for Configuring and Analyzing Evolutionary Algorithms,
Swarm and Evolutionary Computation, 1(1):19-31, 2011
Vision paper about the (embodied) future of evolutionary computing:
A.E. Eiben, S. Kernbach, and Evert Haasdijk
Embodied artificial evolution: Artificial evolutionary systems in the 21st Century,
Evolutionary Intelligence, 2012, DOI: 10.1007/s12065-012-0071-x
RESEARCH
My academic research lies within computational intelligence or
natural computing with evolutionary
computing as the binding factor,
see the thematic overview on the right hand side of this page. I
was/am
involved in various European research projects: EvoNet I
(Esprit 20996), EvoNet II (FP5, IST-1999-14087), DREAM
(FP5, IST-1999-12679), NEW TIES (FP6-502386), SYMBRION (FP7-ICT-2007.8.2), EVOBODY
(FP7-258334),OPTI-FOX
(FP7-123456), AWARE (FP7-123456)
Further to academic research, I have worked in business intelligence
R&D projects, including data warehousing and data
mining for feature selection,
creditibility assessment, direct marketing, customer retention
analysis, sensory data analysis, e-business, etc. Much of this work is
unpublished.
TEACHING
Presently
I teach the course Evolutionary
Computing
and Heuristics
on
the VU Amsterdam. I am also supervising Bachelor projects, Master
Thesis projects and PhD students.
Previously, I have been
teaching university courses on formal logic, search
techniques, evolutionary computing, artificial life, evolutionary
economy, and business
intelligence on the Eindhoven University of Technology, the
Utrecht University, Leiden University, and the VU Amsterdam. I was also
lecturer of a business intelligence course for the
e-Commerce Masters programme of the EUR, Rotterdam and for different
business classes on the Universiteit Nyenrode, The
Netherlands Business School.
ORGANIZATION AND MANAGEMENT
- SCIENCE MANAGEMENT
- EDITORSHIPS
- INTERNATIONAL
EVENTS
- Genetic
and Evolutionary Computation Conference (GECCO
2011), Dublin, 2011, GA track chair
- 2005 Congress
on Evolutionary Computation (CEC'2005),
Edinburgh, 2005, technical chair
- EvoNet
EuropeanSummer
School in Evolutionary Computing, Szeged, 2002,
general chair
- Sixth
International Conference
on Parallel Problem Solving from Nature (PPSN-VI),
Paris, 2000, tutorial chair
- 2000 Congress
on Evolutionary Computation (CEC'2000),
San Diego, 2000, tutorial chair
- Genetic
and Evolutionary Computation Conference (GECCO 1999),
Orlando, 1999, program chair
- Fifth
International Conference
on Parallel Problem Solving from Nature (PPSN-V),
Amsterdam, 1998, general chair
- Seventh Annual Conference
on Evolutionary Programming (EP'98), San
Diego,1998, program chair
- Fifth International
Workshop on the Foundations of Genetic Algorithms (FOGA),
Leiden, 1998, local chair
Multi-parent
recombination
The "orgy in the computer" stuff. Using more than two parents
can greatly improve (speed up) an evolutionary algorithm.
Read
more
Selected publications:
A.E. Eiben,
Multiparent
recombination, Handbook of EC
Parameter
Control (on-line)
Changing EA parameter values during a run offers the greatest
flexibility. Perhaps also the best performance. But it poses great
challenges to EA designers. See the award winning paper that
shaped the field.
Read
more
Selected publications:
A.E. Eiben, R. Hinterding, and Z. Michalewicz,
Parameter
control in evolutionary algorithms, IEEE Transactions of EC
Parameter
Tuning (off-line)
There are many good EAs without parameter control, but there are no
good EAs without well tuned parameters. Parameter tuning (before a
run) is a must for good perfomance. See here the ins and outs of tuning
and a good tuning toolbox
Read
more
Selected publications:
A.E. Eiben and S.K. Smit,
Parameter
Tuning for Configuring and Analyzing Evolutionary Algorithms,
Journal of Swarm and Evolutionary Computing
Evolution
in Time and Space
Situated evolution, embodied evolution, distributed evolutionary
algorithms, p2p evolution and the like. Can be much more challenging
than a simple
genetic algorithm solving the TSP...
Read
more
Selected publications:
J.L.J. Laredo, A.E. Eiben, M. van Steen, and J. J. Merelo,
EvAg:
a scalable peer-to-peer evolutionary algorithm,
Genetic Programming and
Evolvable Machines
Constraint
solving
Evolutionary algorithms are usually blind to constraints. But many
challenging problems are constrained. And there are some good
tricks...
Read
more
Selected publications:
A.E. Eiben, J.K. van der Hauw, and J.I. van Hemert.
Graph
coloring with adaptive evolutionary algorithms, Journal of
Heuristics