Advances in Genetic Programming: Volume 2 PART IV

Advances in Genetic Programming: Volume 2 MIT Press ISBN 0-262-01158-1, 538 pp. $50.00, hardbound. MIT Press 1996 Fall catalog, order code ANGAH2, now available.

edited by Peter J. Angeline Kenneth E. Kinnear, Jr.

IV NEW ENVIRONMENTS FOR GENETIC PROGRAMMING 415
21 Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from SAR Images of Arctic Ice 417
Jason M. Daida, Jonathan D. Hommes, Tommaso F. Bersano-Begey, Steven J. Ross, John F. Vesecky
22 Genetic Programming Learning and the Cobweb Model 443
Shu-Heng Chen, Chia-Hsuan Yeh
23 Evolutionary Identification of Macro-Mechanical Models 467
Marc Schoenauer, Michele Sebag, Francois Jouve, Bertrand Lamy, Habibou Maitournam
This chapter illustrates the potential of genetic programming (GP) in the field of macro-mechanical modeling, addressing the problem of identification of a mechanical model for a material. Two kinds of models are considered. One-dimensional dynamic models are represented via symbolic formulations termed rheological models, which are directly evolved by GP. Three-dimensional static models of hyperelastic materials are expressed in terms of strain energy functions. A model is rated based on the distance between the behavior predicted by the model, and the actual behavior of the material given by a set of mechanical experiments. The choice of GP is motivated by strong arguments, relying on the tree-structure of rheological models in the first case, and on the need for first and second order derivatives in the second case. Key issues are the exploration of viable individuals only, and the use of Gaussian mutations to optimize numerical constants.
24 Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification 489
Brij Masand, Gregory Pietetsky-Shapiro
APPENDICES
A Genetic Programming Resources on the World-Wide Web 499
Patrick Tufts
B A Bibliography for Genetic Programming 507
William B. Langdon
411 references
Index 533

Advances in Genetic Programming: Volume 2

Other Books on Genetic Programming

W.B.Langdon@cs.bham.ac.uk 23 March 2001


IV	NEW ENVIRONMENTS FOR GENETIC PROGRAMMING	415
21	Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from SAR Images of Arctic Ice	417
	Jason M. Daida, Jonathan D. Hommes, Tommaso F. Bersano-Begey, Steven J. Ross, John F. Vesecky
22	Genetic Programming Learning and the Cobweb Model	443
	Shu-Heng Chen, Chia-Hsuan Yeh
23	Evolutionary Identification of Macro-Mechanical Models	467
	Marc Schoenauer, Michele Sebag, Francois Jouve, Bertrand Lamy, Habibou Maitournam This chapter illustrates the potential of genetic programming (GP) in the field of macro-mechanical modeling, addressing the problem of identification of a mechanical model for a material. Two kinds of models are considered. One-dimensional dynamic models are represented via symbolic formulations termed rheological models, which are directly evolved by GP. Three-dimensional static models of hyperelastic materials are expressed in terms of strain energy functions. A model is rated based on the distance between the behavior predicted by the model, and the actual behavior of the material given by a set of mechanical experiments. The choice of GP is motivated by strong arguments, relying on the tree-structure of rheological models in the first case, and on the need for first and second order derivatives in the second case. Key issues are the exploration of viable individuals only, and the use of Gaussian mutations to optimize numerical constants.
24	Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification	489
	Brij Masand, Gregory Pietetsky-Shapiro
	APPENDICES
A	Genetic Programming Resources on the World-Wide Web	499
	Patrick Tufts
B	A Bibliography for Genetic Programming	507
	William B. Langdon 411 references
	Index	533