The LCS and GBML community stop
Today’s New York Times features an article describing a new discovery about the element boron, made in part by evolutionary computing. Full details of the discovery are provided in a January 29, 2009 letter in Nature (subscription required). They (Oganov et al.) used a special purpose evolutionary algorithm called USPEX that is not really described in the Nature piece, but it is described elsewhere including here.
Today’s New York Times features an article describing a new discovery about the element boron, made in part by evolutionary computing. Full details of the discovery are provided in a January 29, 2009 letter in Nature (subscription required). They (Oganov et al.) used a special purpose evolutionary algorithm called USPEX that is not really described in the Nature piece, but it is described elsewhere including here.
The present issue includes three full research articles and two book reviews.In “Scaling of Program Functionality” W. B. Langdon provides a novel theoretical analysis of the relations between size and functionality for several classes of programs. Many aspects of his analysis apply to all possible systems that search for computer programs, but Dr. Langdon also describes specific implications of his analysis for genetic programming and provides experimental confirmation of his results.In “An improved representation for evolving programs” M. S. Withall, C.J. Hinde, and R. G. Stone describe a new representation for evolving programs that combines features of traditional linear and tree-based representations. They present the results of several experiments using their new representation and they discuss implications for the scalability of genetic programming to more complex problems.In “Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming” P. Balasubramaniam and A. Vincent Antony Kumar show how genetic programming can be used to solve differential equations of a particular important class. They compare the genetic programming approach to the traditional Runge Kutta method and they provide experimental confirmation of efficiency improvements.The book reviews in this issue, edited by W. B. Langdon, cover two edited volumes: The Mechanical Mind in History, which was edited by P. Husbands, O. Holland and M. Wheeler (reviewed by P. Collet), and Evolutionary Computation in Practice: Studies in Computational Intelligence, which was edited by T. Yu, D. Davis, C. Baydar, and R. Roy (reviewed by L. M. Deschaine).
The present issue includes three full research articles and two book reviews.In “Scaling of Program Functionality” W. B. Langdon provides a novel theoretical analysis of the relations between size and functionality for several classes of programs. Many aspects of his analysis apply to all possible systems that search for computer programs, but Dr. Langdon also describes specific implications of his analysis for genetic programming and provides experimental confirmation of his results.In “An improved representation for evolving programs” M. S. Withall, C.J. Hinde, and R. G. Stone describe a new representation for evolving programs that combines features of traditional linear and tree-based representations. They present the results of several experiments using their new representation and they discuss implications for the scalability of genetic programming to more complex problems.In “Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming” P. Balasubramaniam and A. Vincent Antony Kumar show how genetic programming can be used to solve differential equations of a particular important class. They compare the genetic programming approach to the traditional Runge Kutta method and they provide experimental confirmation of efficiency improvements.The book reviews in this issue, edited by W. B. Langdon, cover two edited volumes: The Mechanical Mind in History, which was edited by P. Husbands, O. Holland and M. Wheeler (reviewed by P. Collet), and Evolutionary Computation in Practice: Studies in Computational Intelligence, which was edited by T. Yu, D. Davis, C. Baydar, and R. Roy (reviewed by L. M. Deschaine).
by Llorà , X.
IlliGAL technical report 2009001. You can download the pdf here. More information is also available at the Meandre website as part of the SEASR project.
Abstract:Â Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing […]
by Llorà , X.
IlliGAL technical report 2009001. You can download the pdf here. More information is also available at the Meandre website as part of the SEASR project.
Abstract:Â Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study […]
Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org
The issue features:
Solving Complex Problems in Human Genetics Using GP
by Casey S. Greene and Jason H. Moore
A Camera Obscura for Ants by Carlos M. Fernandes
GEVA: Grammatical Evolution in Java by Michael O’Neill et. al.
Events Reports: ICSE-2008
Forthcoming papers
Calls & […]
The new issue of SIGEVOlution is now available for you to download from:
The issue features:
The newsletter is intended to be viewed electronically.
Pier Luca Lanzi (EIC)
Acknowledgment
Content Type Journal ArticleDOI 10.1007/s10710-008-9078-6Authors
Lee Spector, Hampshire College School of Cognitive Science Amherst MA 01002 USA
Journal Genetic Programming and Evolvable MachinesOnline ISSN 1573-7632Print ISSN …
Acknowledgment
cForms is a WordPress plugin that allows you to quickly add forms on your site. The plugin will allow you to collect information from visitors easily. Besides mail notifications, it also provides database back-ended tracking. Pretty convenient if you have to boost the usefulness of your site. Two examples I have more or less been involved is […]
cForms is a WordPress plugin that allows you to quickly add forms on your site. The plugin will allow you to collect information from visitors easily. Besides mail notifications, it also provides database back-ended tracking. Pretty convenient if you have to boost the usefulness of your site. Two examples I have more or less been involved is the SEASR 2009 workshop registration form and the iFoundry application form. On both cases, cForms worked like a champ.
I have been using FireStats for gathering statistics on WordPress sites for more than a couple of years now. I mainly use FireStats combined with WordPress Stats, and Google Analytics. Each of them give you different views into traffic, but FireStats is by far quick and fast and give you a good overall picture you can dig down using WordPress […]
I have been using FireStats for gathering statistics on WordPress sites for more than a couple of years now. I mainly use FireStats combined with WordPress Stats, and Google Analytics. Each of them give you different views into traffic, but FireStats is by far quick and fast and give you a good overall picture you can dig down using WordPress Stats and Google Analytics. I just installed the new version 1.6.0 on this blog and found a new interesting goodie. Now FireStats also tracks the number of RSS readers coming to your site 
I just ran into the comic strip below at xkcd. I am still laughing now. Fitness functions are tricky. Once somebody told me that genetic algorithms always get their target; the main problem is to explain what the target is. If you want to learn a “fountain pen”, you better be accurate defining it or […]
I just ran into the comic strip below at xkcd. I am still laughing now. Fitness functions are tricky. Once somebody told me that genetic algorithms always get their target; the main problem is to explain what the target is. If you want to learn a “fountain pen”, you better be accurate defining it or you may end up getting an unexpected all-terrain “pencil”. Yes, I know the example is quite solution free, but still has some truth to it. How many times have you end up getting something you did not expect, only because evolution find a better fitted crack in your description? Anyway, it is fun what you end running into on the Internet 
I just ran into the comic strip below at xkcd. I am still laughing now. Fitness functions are tricky. Once somebody told me that genetic algorithms always get their target; the main problem is to explain what the target is. If you want to learn a “fountain pen”, you better be accurate defining it or […]
I just ran into the comic strip below at xkcd. I am still laughing now. Fitness functions are tricky. Once somebody told me that genetic algorithms always get their target; the main problem is to explain what the target is. If you want to learn a “fountain pen”, you better be accurate defining it or you may end up getting an unexpected all-terrain “pencil”. Yes, I know the example is quite solution free, but still has some truth to it. How many times have you end up getting something you did not expect, only because evolution find a better fitted crack in your description? Anyway, it is fun what you end running into on the Internet
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