About the GPEMjournal blog

This is the editor's blog for the journal Genetic Programming and Evolvable Machines. The official web site for the journal, maintained by the publisher (Springer) is here. The GPEMjournal blog is authored and maintained by Lee Spector.

Saturday, April 6, 2013

GPEM 14(2) available online


The second issue of volume 14 of Genetic Programming and Evolvable Machines is now available online, containing:

"Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework"
by Michele Amoretti

"Inference of hidden variables in systems of differential equations with genetic programming"
by Theodore W. Cornforth and Hod Lipson

"A module-level three-stage approach to the evolutionary design of sequential logic circuits"
by Yanyun Tao, Yuzhen Zhang, Jian Cao and Yalong Huang

"Improving analytical models of circular concrete columns with genetic programming polynomials"
by Hsing-Chih Tsai and Chan-Ping Pan

"Controllable procedural map generation via multiobjective evolution"
by Julian Togelius, Mike Preuss, Nicola Beume, Simon Wessing, Johan Hagelbäck, Georgios N. Yannakakis & Corrado Grappiolo

BOOK REVIEW
"John H. Holland: Signals and boundaries: building blocks for complex adaptive systems"
by Denis Robilliard




Friday, March 8, 2013

Alma Lilia Garcia Almanza and Edward Tsang reply to Blake Lebaron's book review

Edward Tsang and Alma Lilia Garcia Almanza have written the following reply to the review of their book, Evolutionary applications for financial prediction: classification methods to gather patterns using genetic programming, which was written by Blake Lebaron and published in Volume 13, Issue 4 of Genetic Programming and Evolvable Machines:

It is good to receive a review of our book by an expert in the field. The review is insightful. It highlights the strength as well as weakness of the book. Here we would like to address LeBaron's major concern: "... My biggest concern is that the authors should have tried some more standard financial forecasting tests. This would include setting these classifiers up as a trading rule, and seeing how well they do."

Let us emphasize that our main goal is to prove that the new methods improve over genetic programming. For example, given a set of decision generated by genetic programming, the Repository Method will generate a rule set with better predictive performance. Given that genetic programming is an important technique in machine learning, our improvement on it is significant. We made no attempt to establish our methods to be "the best methods for forecasting". That is the reason why we have not compared it with trading rules generated by other techniques and test benchmarks. 

Besides, one major difficulty in comparing our methods against others is that our approaches were designed to generate a set of results (rules)  to satisfy different levels of risk preferences. The results of our methods are plotted in ROC curves. Other predictive techniques would typically generate just one prediction rule, which means they would produce just one point in the ROC space; hence fair comparison would be difficult.

The onus is on the authors to make our objectives clear. Lebaron's review suggests that we have not explained the above points clearly. We are therefore grateful to the reviewer and the Journal to give us a chance to clarify them.


Wednesday, February 13, 2013

GPEM 14(1) available online



The first issue of volume 14 of Genetic Programming and Evolvable Machines is now available online, containing:

"Introduction"
by Lee Spector

"Better GP benchmarks: community survey results and proposals"
by David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Ja?kowski, Una-May O’Reilly & Sean Luke

"Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators"
by Krzysztof Krawiec & Tomasz Pawlak

"A comparison of grammatical genetic programming grammars for controlling femtocell network coverage"
by Erik Hemberg, Lester Ho, Michael O’Neill & Holger Claussen

"Multi-objective optimization of QCA circuits with multiple outputs using genetic programming"
by Razieh Rezaee, Mahboobeh Houshmand & Monireh Houshmand

BOOK REVIEW

"Franz Rothlauf: Design of Modern Heuristics"
by Dario Landa-Silva

BOOK REVIEW
"Andrew Adamatzky: Physarum Machines: Computers from Slime Mould"
by Vincenzo Bonifaci

"Acknowledgment"
by Lee Spector


Tuesday, December 18, 2012

Deadline extended for Special Issue on Evolvability and Robustness in Artificial Evolving Systems

The deadline for submissions to the Special Issue on Evolvability and Robustness in Artificial Evolving Systems has been extended to April 30, 2013. Please see the Call for Papers [PDF] for additional details.

GPEM 13(4) available online


The fourth issue of volume 13 of Genetic Programming and Evolvable Machines is now available online, containing:

"Open-ended evolution to discover analogue circuits for beyond conventional applications"
by Yerbol A. Sapargaliyev and Tatiana G. Kalganova

"Quantum control experiments as a testbed for evolutionary multi-objective algorithms"
by Ofer M. Shir, Jonathan Roslund, Zaki Leghtas, and Herschel Rabitz

"Evolving team behaviors with specialization"
by G. S. Nitschke, A. E. Eiben, and M. C. Schut

BOOK REVIEW

"Alma Lilia Garcia Almanza and Edward Tsang: Evolutionary applications for financial prediction: classification methods to gather patterns using genetic programming"
by Blake LeBaron

Monday, July 30, 2012

SIGEVOlution Volume 6, Issue 1, is now available


The SIGEVOlution newsletter Volume 6 Issue 1 is now available for download from: http://www.sigevolution.org
The new issue features:


  • "Lessons from an App Ecosystem" by Soo Ling Lim & Peter J. Bentley
  • "Distilling GeneChips" by William B. Langdon
  • Calls and calendar


The newsletter is intended to be viewed electronically.
Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

Sunday, July 29, 2012

Renewed and expanded editorial board

I'm very happy to report that we have completed our recent process of renewing and expanding the GPEM editorial board, with the new full board being as follows:


Editor-in-Chief:Lee Spector, Hampshire College, USA
Founding Editor:
Wolfgang Banzhaf, Memorial University of Newfoundland, St. John's, Canada
Resource Review Editor:William B. Langdon, University College London, UK
Advisory Board:David Goldberg, University of Illinois, USA
Erik Goodman, Michigan State University, USA
John Holland, University of Michigan, USA
John Koza, Stanford University, USA
Pierre Marchal, CSEM, Neuchâtel, Switzerland
Ingo Rechenberg, Technical University of Berlin, Germany
Thematic Area Editors:
Area Editor for Life Sciences
James A. Foster, University of Idaho, USA
Area Editor for Data Analytics and Knowledge Discovery
Una-May O'Reilly, Massachusetts Institute of Technology, USA
Area Editor for Games
Moshe Sipper, Ben-Gurion University, Israel
Associate Editors:James A. Foster, University of Idaho, USAPauline C. Haddow, The Norwegian University of Science and Technology, Norway
Hitoshi Iba, University of Tokyo, Japan
William B. Langdon, University College London, UK
Julian Miller, University of York, UK
Una-May O'Reilly, Massachusetts Institute of Technology, USA
Riccardo Poli, University of Essex, UK
Moshe Sipper, Ben-Gurion University, Israel
Stephen Smith, University of York, UKTerence Soule, University of Idaho, USA
Marco Tomassini, University of Lausanne, Switzerland
Andy Tyrrell, University of York, UK
Leonardo Vanneschi, ISEGI, Universidade Nova de Lisboa, Portugal
Editorial Board:L. Altenberg, University of Hawaii at Manoa, USA
S. Cagnoni, University of Parma, Italy
K. Deb, Indian Institute of Technology, India
M. Dorigo, Free University of Brussels, Belgium
M. Ebner, Eberhard Karls Universität Tübingen, Germany
A. Eiben, Vrije Universiteit Amsterdam, The Netherlands
A. Ekart, Aston University, UK
D. Floreano, Swiss Federal Institute of Technology (EPFL), Switzerland
C. Gagne, Laval University, Canada
S.M. Gustafson, GE Global Research, USA
S. Harding, Machine Intelligence, Ltd., UK
I. Harvey, University of Sussex, UK
M. Heywood, Dalhousie University, Canada
C. Jacob, The University of Calgary, Canada
C. Johnson, University of Kent, UK
T. Kalganova, Brunel University, UK
M. Keijzer, Prognosys, Netherlands
D.B. Kell, UMIST, UK
D. Keymeulen, Jet Propulsion Laboratory, USA
K. Krawiec, Poznan University of Technology, Poland
A. Leier, ETH Zurich, Switzerland
S. Luke, George Mason University, USA
P. Machado, University of Coimbra, Portugal
T. McConaghy, Solido Design Automation, Inc., Canada
R. McKay, Seoul National University, Korea
N.F. McPhee, University of Minnesota at Morris, USA
M. Mitchell, Portland State University, USA
D. Montana, BBN Inc., USA
J. Moore, Dartmouth Medical School, Lebanon, NH
A. Moraglio, University of Birmingham, UK
M. Nicolau, University College Dublin, Ireland
M. O'Neill, University College Dublin, Ireland
C. Ryan, University of Limerick, Ireland
M. Schoenauer, I.N.R.I.A. Futurs, France
L. Sekanina, Brno University of Technology, Czech Republic
S. Silva, INESC-ID Lisboa, Portugal, Portugal
G. Squillero, Polytechnic University of Turin, Italy
C. Stephens, UNAM, Mexico
A. Stoica, California Institute of Technology, USA
I. Taney, Doshisha University, Japan
G. Tempesti, University of York, UK
M. Trefzer, University of York, UK
E. Vladislavleva, Evolved Analytics Europe BVBA, Belgium
P. Whigham, University of Otago, New Zealand
M.-L. Wong, Lingnan University, Hong Kong
T. Yu, Memorial University of Newfoundland, Canada
B. Zhang, Seoul National University, Korea
M. Zhang, Victoria University of Wellington, New Zealand