GPEM 13(1) now available online

The first issue of volume 13 of Genetic Programming and Evolvable Machines is now available online. It includes a special section on "Evolutionary algorithms for data mining" guest edited by Pierre Collet and Man Leung Wong, along with several other items of interest. The full contents are:

EDITORIAL
"Editorial introduction"
by Lee Spector

"The evolution of higher-level biochemical reaction models"
by Brian J. Ross

"Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection"
by Khaled Badran and Peter Rockett

SOFTWARE REVIEW
"Software review: the ECJ toolkit"
by David R. White

EDITORIAL
"Evolutionary algorithms for data mining"
by Pierre Collet and Man Leung Wong

"Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces"
by John A. Doucette, Andrew R. McIntyre, Peter Lichodzijewski and Malcolm I. Heywood

"Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization"
by Kalyan Veeramachaneni, Ekaterina Vladislavleva and Una-May O’Reilly

ACKNOWLEDGMENT

MIT story on GPEM article on sensory evaluation data

There's a nice story on MIT news, "The mathematics of taste," which is also being picked up on some other sites, about the GPEM article on "Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization" by Kalyan Veeramachaneni, Ekaterina Vladislavleva and Una-May O’Reilly.

Eureqa in Science News

There's a nice little piece in the January 14, 2012 issue of Science News on Lipson and Schmidt's Eureqa system. (h/t to Stuart Card for the pointer.)

New Book (Free): "Evolved to Win", by Moshe Sipper

Recent years have seen a sharp increase in the application of evolutionary computation techniques within the domain of games. Situated at the forefront of this research tidal wave, Moshe Sipper and his group have produced a plethora of award-winning results, in numerous games of diverse natures, evidencing the success and efficiency of evolutionary algorithms in general­—and genetic programming in particular—at producing top-notch, human-competitive game strategies. From classic chess and checkers, through simulated car racing and virtual warfare, to mind-bending puzzles, this book serves both as a tour de force of the research landscape and as a guide to the application of evolutionary computation within the domain of games.

An outstanding, timely book in the rapidly growing area of computational intelligence in games. A must read for both the neophyte and the seasoned researcher, with all the hallmarks of a landmark book.

John Koza, author of Genetic Programming tetralogy

In Evolved to Win Moshe Sipper provides a treasure trove of detailed examples and advice on using evolutionary computation, in conjunction with human expertise, to solve hard puzzles and to win a wide variety of challenging games. Sipper and his colleagues know this field better than anyone else, having produced some of the field's strongest and most exciting results, and this book provides a comprehensive tour of their results along with ample guidance for newcomers to the field.

Lee Spector, Professor of Computer Science, Hampshire College, and Editor-in-Chief of the journal Genetic Programming and Evolvable Machines

Free download Hard copy

citing Journal > conference > tech reports

I have seen recently several papers which cite technical reports.
May be there are good reasons for this but it was my understanding
that where work is available in a number of places we should cite
journal articles before conference papers and only cite technical
reports if the work is not otherwise available.

Bill

Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent's letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach", of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores "simulators that cheat", the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section "SPICE can lie".)

The broader issue -- trustworthy synthesis -- is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 -- Trent McConaghy, October 3, 2011

New impact factor

The journal received its first impact factor from Journal Citation Reports last year (2010), for the 2009 publication year. Now (in 2011) we have new numbers for the 2010 publication year. They have improved somewhat and are, I think, strong for a journal as young as Genetic Programming and Evolvable Machines:

Impact factor: 1.167
Rank in category: Artificial Intelligence: 63 out of 108
Rank in category: Theory and Methods: 41 out of 97

Journal Citation Reports also provides an Immediacy Index, for which our numbers are:

Immediacy index: 0.143
Rank in category: Artificial Intelligence: 64 out of 108
Rank in category: Theory and Methods: 52 out of 97