Genetic Programming Theory and Practice VI
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Beschreibung
Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP).
Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.
These contributions address several significant inter-dependent themes which emerged from this year's workshop, including:
Making efficient and effective use of test data
Sustaining the long term evolvability of our GP systems
Exploiting discovered subsolutions for reuse
Increasing the role of a Domain Expert
In the course of investigating these themes, the chapters describe a variety of techniques in widespread use among practitioners who deal with industrial-scale, real-world problems, such as:
Pareto optimization, particularly as a means to limit solution complexity
Various types of age-layered populations or niching mechanisms
Data partitioning, a priori or adaptively, e.g., via co-evolution
Cluster computing or general purpose graphics processors for parallel computing
Ensemble/team solutions
This work covers applications of GP to a host of domains, including bioinformatics, symbolic regression for system modeling in various settings, circuit design, and financial modeling to support portfolio management.
This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.
These contributions address several significant interdependent themes which emerged from this year’s workshop, including: (1) Making efficient and effective use of test data. (2) Sustaining the long-term evolvability of our GP systems. (3) Exploiting discovered subsolutions for reuse. (4) Increasing the role of a Domain Expert.
Discusses the hurdles faced in solving large-scale, cutting-edge applications Provides in depth presentations of the latest and most significant applications of GP and the most recent theoretical results with direct applicability to state-of-the-art problems Contributions by GP theorists from major universities and active practitioners from industry examine how GP theory informs practice and how GP practice impacts GP theory
Autor*in
Rick Riolo
Themen in »Genetic Programming Theory and Practice VI«
Artificial Intelligence Automatic Programming Computer Science Evolution of Models Evolutionary Computation Genetic Algorithms Problem Solving Search Algorithms Symbolic Regression algorithms classification complexity evolution genetic programming knowledge
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From the reviews:
"This book is a collection of contributions from various theorists and practitioners working in the genetic programming (GP) field. … anyone who is interested in the subject should monitor the ongoing research; this volume is an essential assistant for the task. … I found the book interesting … that stimulate the researcher to think of further applications of the presented techniques and methodologies. … I recommend it not only to GP researchers and practitioners, but also to any scientist dealing with optimization problems … ." (Lefteris Angelis, ACM Computing Reviews, March, 2009)
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