BibSonomy publications for /author/Dirk/geneticThu Jun 19 17:46:40 CEST 2008Orlando, Florida, USAProceedings of the Genetic and Evolutionary Computation Conference13-17 July643--648Estimating the Significant Non-Linearities in the Genome Problem-Coding11999algorithms and classifier genetic systems Thu Jun 19 17:35:00 CEST 2008Orlando, Florida, USAProceedings of the Genetic and Evolutionary Computation Conference13-17 July665--669The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements11999algorithms and classifier genetic systems Thu Jun 19 17:35:00 CEST 2008Dortmund, Germany50/98Hierarchical Genetic Programming Using Local ModulesTechnical Report1998algorithms, genetic programming This paper presents detailed experimental results for a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural hierarchy in that local modules themselves may define local sub-modules.Thu Jun 19 17:35:00 CEST 2008InterJournal Complex SystemsHierarchical Genetic Programming using Local Modules2282000algorithms, genetic programming This paper presents a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural recursiveness in that local modules themselves may define local sub-modules.Thu Jun 19 17:35:00 CEST 2008Vienna, AustriaEUROSIM'95Self-organizing modeling of biotechnological batch and fed-batch fermentations1995algorithms, biotechnology fermentation, genetic programming, An approach for the automatic generation of dynamic nonlinear process models obtained from experimantal process data and theoretical biological and chemical reflections using genetic programming for the supervision and coordination of the symbolic model structure during automatic development BioX++ includes (amongs fuzzy rule learning, expert system, NN also refered to) GP to produce process models, constants adapted using standard algorithmic techniques.Thu Jun 19 17:35:00 CEST 2008Orlando, Florida, USAProceedings of the Genetic and Evolutionary Computation Conference13-17 July60--67Linkage Information Processing In Distribution Estimation Algorithms11999algorithms and classifier genetic systems Thu Jun 19 17:35:00 CEST 2008EdinburghGenetic Programming, Proceedings of EuroGP'200015-16 April29--43LNCSMetric Based Evolutionary Algorithms18022000algorithms, genetic programming In this article a set of guidelines for the design of genetic operators and the representation of the phenotype space is proposed. These guidelines should help to systematize the design of problem-specific evolutionary algorithms. Hence, they should be particularly beneficial for the design of genetic programming systems. The applicability of this concept is shown by the systematic design of a genetic programming system for finding Boolean functions. This system is the first GP-system, that reportedly found the 12 parity function.Thu Jun 19 17:35:00 CEST 2008GermanyJulyCI-41/98On Representation and Genetic Operators in Evolutionary AlgorithmsComputational Intelligence1998algorithms, genetic programming The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a suitable representation of the variable space and appropriate genetic operators. In practice mainly problemspecific representations with specific genetic operators and miscellaneous extensions can be observed. In this connection it attracts attention that hardly any formal requirements on the genetic operators are stated. In this article we first formalize the representation problem and then propose a package of requirements to guide the design of genetic operators. By the definition of distance measures on the geno- and phenotype space it is possible to integrate problem-specific knowledge into the genetic operators. As an example we show how this package of requirements can be used to design a genetic programming (GP) system for finding Boolean functions.Thu Jun 19 17:35:00 CEST 2008Stanford University, CA, USAGenetic Programming 1997: Proceedings of the Second Annual Conference13-16 July102--110Solving Problems of Combinatory Logic with Genetic Programming1997algorithms, genetic programming Thu Jun 19 17:35:00 CEST 2008Orlando, Florida, USAProceedings of the Genetic and Evolutionary Computation Conference13-17 July1027--1032Generating Lemmas for Tableau-based Proof Search Using Genetic Programming21999algorithms, and evolvable genetic hardware programming Thu Jun 19 17:35:00 CEST 2008at -- Automatisierungstechnik6277--288{Selbstorganisierende Generierung strukturierter Prozemodelle}431995Modellbildung Selbstorganisierende algorithms, genetic programming,