Del 0 - Lecture Notes in Computer Science

Genetic Programming

6th European Conference, EuroGP 2003, Essex, UK, April 14-16, 2003. Proceedings

Häftad, Engelska, 2003

AvConor Ryan,Terence Soule,Riccardo Poli,Edward Tsang,Maarten Keijzer,Ernesto Costa

1 439 kr

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This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields.

Produktinformation

Utgivningsdatum2003-04-07
Mått163 x 242 x 28 mm
Vikt819 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor490
Upplaga2003
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540009719

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Systemvetenskap och AI inom Data och IT

Talks.- Evolving Cellular Automata to Grow Microstructures.- An Innovative Application of a Constrained-Syntax Genetic Programming System to the Problem of Predicting Survival of Patients.- New Factorial Design Theoretic Crossover Operator for Parametrical Problem.- Overfitting or Poor Learning: A Critique of Current Financial Applications of GP.- Evolutionary Design of Objects Using Scene Graphs.- Ensemble Techniques for Parallel Genetic Programming Based Classifiers.- Improving Symbolic Regression with Interval Arithmetic and Linear Scaling.- Evolving Hierarchical and Recursive Teleo-reactive Programs through Genetic Programming.- Interactive GP for Data Retrieval in Medical Databases.- Parallel Programs Are More Evolvable than Sequential Programs.- Genetic Programming with Meta-search: Searching for a Successful Population within the Classification Domain.- Evolving Finite State Transducers: Some Initial Explorations.- Reducing Population Size while Maintaining Diversity.- How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System.- More on Computational Effort Statistics for Genetic Programming.- Analysis of a Digit Concatenation Approach to Constant Creation.- Genetic Programming with Boosting for Ambiguities in Regression Problems.- Maximum Homologous Crossover for Linear Genetic Programming.- A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming.- Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding.- Feature Construction and Selection Using Genetic Programming and a Genetic Algorithm.- Genetic Programming Applied to Compiler Heuristic Optimization.- Modularity in Genetic Programming.- Decreasing the Number of Evaluations in Evolutionary Algorithms by Using a Meta-model of the Fitness Function.-Posters.- Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution.- Neutral Variations Cause Bloat in Linear GP.- Experimental Design Based Multi-parent Crossover Operator.- An Enhanced Framework for Microprocessor Test-Program Generation.- The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations.- Multi Niche Parallel GP with a Junk-Code Migration Model.- Tree Adjoining Grammars, Language Bias, and Genetic Programming.- Artificial Immune System Programming for Symbolic Regression.- Grammatical Evolution with Bidirectional Representation.- Introducing a Perl Genetic Programming System - and Can Meta-evolution Solve the Bloat Problem?.- Evolutionary Optimized Mold Temperature Control Strategies Using a Multi-polyline Approach.- Genetic Programming for Attribute Construction in Data Mining.- Sensible Initialisation in Chorus.- An Analysis of Diversity of Constants of Genetic Programming.- Research of a Cellular Automaton Simulating Logic Gates by Evolutionary Algorithms.- From Implementations to a General Concept of Evolvable Machines.- Cooperative Evolution on the Intertwined Spirals Problem.- The Root Causes of Code Growth in Genetic Programming.- Fitness Distance Correlation in Structural Mutation Genetic Programming.- Disease Modeling Using Evolved Discriminate Function.- No Free Lunch, Program Induction and Combinatorial Problems.

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