English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Report

Genetic algorithms within the framework of evolutionary computation: Proceedings of the KI-94 Workshop

MPS-Authors
/persons/resource/persons44640

Hopf,  Jörn
Programming Logics, MPI for Informatics, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

MPI-I-94-241fertig.pdf
(Any fulltext), 101MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Hopf, J. (Ed.).(1994). Genetic algorithms within the framework of evolutionary computation: Proceedings of the KI-94 Workshop (MPI-I-94-241). Saarbrücken: Max-Planck-Institut für Informatik.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-B7BE-2
Abstract
It is a matter of fact that in Europe evolution strategies and in the U.S.A. genetic algorithms have survived more than a decade of non-acceptance or neglect. It is also true, however, that so far both strata of ideas have evolved in geographical isolation and thus have not led to recombined offspring. Now it is time for a new generation of algorithms which make use of the rich gene pool of ideas on both sides of the Atlantic.\\ It is certain that today there are three different schools whose roots have developed independently from each other: \begin{itemize} \item Evolutionary Programming (EP) \item Evolution Strategies (ESs) \item Genetic Algorithms (GAs) \end{itemize} Genetic Programming (GP) and Classifier Systems (CSs) are both special subbranches of the GA philosophy.\\ Only since 1990 contacts between these different schools take place regularly. In 1991, on the fifth International Conference on Genetic Algorithms the generic term {\it Evolutionary Algorithms (EAs)} for EP, ES and GA was agreed on.\\ Evolutionary Computation (EC) does not abandon traditional methods. None of the EAs would do the job better or even as good as those. EC should be taken into consideration if good old and well underpinned methods either do not exist, are not applicable, or fail.\\ Evolutionary approaches play a considerable role in Artificial Life, a divergence from classical Artificial Intelligence, control, planning, combinatorial optimization and many other areas. This workshop surveys the state of the art, presents examples and case studies as well as attempts at systematization. In addition, due to this workshop, a research area which has already tackled real--world problems with considerable success receives more attention.\\ This year the first workshop on EC will take place on the German Annual Conference on Artificial Intelligence ({\sl KI-94}).\\ It starts with two invited lectures: {\sl Quantitative Experimental Studies of Darwinian Evolution} by Christof K.~Biebricher, Max-Planck-Institute for physical Chemistry, one of the leading researcher in evolution and {\sl The Science of Breeding and its Application to the Breeder Genetic Algorithm} by Heinz M{\"u}hlenbein, German National Research Center for Computer Science (GMD), a leading researcher who covers both, fundamental research and real applications. This is followed by two tutorials: {\sl Artificial Life and Selforganisation} by Wolfgang Banzhaf, Dortmund University and {\sl Genetic Programming} by Frank Klawonn, University of Braunschweig.\\ I hope that this gives a little overview and motivation for more reasarch in this field. On the basis of a very good national and international participation our workshop includes the following main topics: \begin{itemize} \item Theory of Evolutionary and Genetic Computation \item Biological Principles \item Genetic Programming \item Artificial Life \item Genetic Algorithms for Neural Network Design \item and many Applications \end{itemize}