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.
Many congratulations to David Goldberg (Advisory Board Member of GPEM) who has been awarded the IEEE's Computational Intelligence SocietyEvolutionary Computation Pioneer Award at IEEE World Congress on Computational Intelligence in Barcelona last week.
Call for Papers: Special Issue on Evolutionary Algorithms for Data Mining
Journal: Genetic Programming and Evolvable Machines (pub. by Springer)
Guest Editors: Pierre Collet, Université de Strasbourg, France; Man Leung Wong, Lingnan University, Hong Kong
Editor-in-Chief: Lee Spector, Hampshire College
The corporate and scientific communities are overwhelmed with an influx of data that is stored in on-line databases. Analyzing these data and extracting meaningful information in a timely fashion is intractable without computer assistance and powerful analytical tools. Data Mining is defined as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in databases (Fayyad et al. 1996). It is an active research area with promise for high payoffs in many business and scientific applications including direct marketing, trend analysis, fraud detection, stock price forecasting, option trading, bond rating, portfolio management, shopping patterns analysis, and medical diagnosis (Simoudis et al. 1996, Groth 1998).
The future of scientific computing is parallel and Artificial Evolution is inherently parallel. Therefore, all the recent successes in Data Mining obtained using Genetic Programming, Genetic Algorithms, Evolutionary Programming, Evolution Strategies, etc. bear a great potential. The grand challenge of using Evolutionary Algorithms for Data Mining is to automatically process large quantities of raw incomplete data with noisy information, identify the most significant and meaningful information/knowledge and present it for achieving the user's goals.
The aim of this special issue is to provide authors with a possibility to publish their work in a renowned journal and the reader with an understanding of the grand challenge, novel approaches in tackling the grand challenge, and some real-life applications, be they already parallelized or not.
We encourage submission of high quality papers (original work that has neither appeared in, nor is under consideration by, other journals), both theoretical and empirical, discussing novel Evolutionary Algorithms for Data Mining. Practical papers that describe successful applications of Evolutionary Algorithms for challenging real-life data mining problems are also sought. Subjects will include (but are not limited to):
- Parallel evolutionary algorithms for data mining
- Data mining from incomplete, imprecise, noisy, imbalanced, and huge databases
- Evolutionary algorithms for cost-sensitive data mining
- Evolutionary ensemble techniques for data mining
- Evolutionary algorithms for supervised and unsupervised classification of data
We encourage all prospective authors to contact the guest editors, at the address below, as early as possible, to indicate your intention to submit a paper to this special issue.
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.
Manuscripts should be submitted to: http://GENP.edmgr.com. Choose "Evol. Algorithms for Data Mining" as the article type when submitting.
Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.
== Guest Editors:
Pierre Collet, Université de Strasbourg, France; email@example.com
Man Leung Wong, Lingnan University, Hong Kong. firstname.lastname@example.org
== Important Dates:
Paper submission deadline: Dec 15, 2010
First Notification: March 15, 2011
Final manuscript: April 30, 2011
Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery in Data Mining, pp. 1-34. Menlo Park, CA: AAAI Press.
Groth, R. (1998). Data Mining: A Hands-On Approach for Business Professionals. Upper Saddle River NJ: Prentice Hall.
Simoudis, E., Han, J., and Fayyd, U. (1996). Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Menlo Park CA: AAAI Press.
I am pleased to announce that the Genetic Programming and Evolvable Machines "Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines," edited by Julian Miller and Riccardo Poli, is now available online.
This is a really nice special issue containing some excellent summaries of the state of the field and directions in which it is expected to move in the near future.
I am also happy to announce that the entire issue is FREE online for the month of July, 2010. In addition, two of the issue's articles (the one by Koza and the one by Langdon Gustafson) are published under Open Access and will therefore be free forever.
The articles in this issue are:
"Editorial to tenth anniversary issue on progress in genetic programming and evolvable machines"
by Julian F. Miller and Riccardo Poli
"Human-competitive results produced by genetic programming"
by John R. Koza
"Theoretical results in genetic programming: the next ten years?"
by Riccardo Poli, Leonardo Vanneschi, William B. Langdon and Nicholas Freitag McPhee
"Genetic Programming and Evolvable Machines: ten years of reviews"
by W. B. Langdon and S. M. Gustafson
"Open issues in genetic programming"
by Michael O’Neill, Leonardo Vanneschi, Steven Gustafson and Wolfgang Banzhaf
"Grammar-based Genetic Programming: a survey"
by Robert I. McKay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan and Michael O’Neill
"Developments in Cartesian Genetic Programming: self-modifying CGP"
by Simon Harding, Julian F. Miller and Wolfgang Banzhaf
Book Review (not part of the special issue per se): "Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies"
by Ivan Garibay
"Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two"
by Marco Tomassini and Leonardo Vanneschi
"An ensemble-based evolutionary framework for coping with distributed intrusion detection"
by Gianluigi Folino, Clara Pizzuti and Giandomenico Spezzano
"Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms"
by Garnett Wilson and Wolfgang Banzhaf
"Simdist: a distribution system for easy parallelization of evolutionary computation"
by Boye Annfelt Høverstad
"Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm"
by Ting Hu, Simon Harding and Wolfgang Banzhaf
"EvAg: a scalable peer-to-peer evolutionary algorithm"
by J. L. J. Laredo, A. E. Eiben, M. van Steen and J. J. Merelo