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Genetic Programming Theory and Practice V / Edition 1
     

Genetic Programming Theory and Practice V / Edition 1

by Rick Riolo, Terence Soule, Bill Worzel
 

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ISBN-10: 1441945474

ISBN-13: 9781441945471

Pub. Date: 11/19/2010

Publisher: Springer US

Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems. It aims to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). This volume is a unique and indispensable tool for academics, researchers and

Overview

Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems. It aims to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.

Product Details

ISBN-13:
9781441945471
Publisher:
Springer US
Publication date:
11/19/2010
Series:
Genetic and Evolutionary Computation Series
Edition description:
Softcover reprint of hardcover 1st ed. 2008
Pages:
279
Product dimensions:
6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

Genetic Programming: Theory and Practice.- Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming.- Manipulation of Convergence in Evolutionary Systems.- Large-Scale, Time-Constrained Symbolic Regression-Classification.- Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction.- Towards an Information Theoretic Framework for Genetic Programming.- Investigating Problem Hardness of Real Life Applications.- Improving the Scalability of Generative Representations for Openended Design.- Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming.- Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design.- Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs.- Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models.- Improving Performance and Cooperation in Multi-Agent Systems.- An Empirical Study of Multi-Objective Algorithms for Sk Ranking.- Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center.

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