Theoretical results in genetic programming: the next ten years?

Abstract  We consider the theoretical results in GP so far and prospective areas for the future. We begin by reviewing the state of
the art in genetic programming (GP) theory including: schema theories, Markov chain models, the distribution …

Abstract  

We consider the theoretical results in GP so far and prospective areas for the future. We begin by reviewing the state of
the art in genetic programming (GP) theory including: schema theories, Markov chain models, the distribution of functionality
in program search spaces, the problem of bloat, the applicability of the no-free-lunch theory to GP, and how we can estimate
the difficulty of problems before actually running the system. We then look at how each of these areas might develop in the
next decade, considering also new possible avenues for theory, the challenges ahead and the open issues.

  • Content Type Journal Article
  • Pages 285-320
  • DOI 10.1007/s10710-010-9110-5
  • Authors
    • Riccardo Poli, University of Essex School of Computer Science and Electronic Engineering Colchester CO4 3SQ UK
    • Leonardo Vanneschi, University of Milano-Bicocca Department of Informatics, Systems and Communication (D.I.S.Co.) viale Sarca 336-U14 Milan Italy
    • William B. Langdon, King’s College London Department of Computer Science London WC2R 2LS UK
    • Nicholas Freitag McPhee, University of Minnesota Morris Division of Science and Mathematics Morris MN 56267 USA

Open issues in genetic programming

Abstract  It is approximately 50 years since the first computational experiments were conducted in what has become known today as the
field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and …

Abstract  

It is approximately 50 years since the first computational experiments were conducted in what has become known today as the
field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the
first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory
and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant
open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in
the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its
full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline
some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate
debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful
problem solving algorithms.

  • Content Type Journal Article
  • Pages 339-363
  • DOI 10.1007/s10710-010-9113-2
  • Authors
    • Michael O’Neill, University College Dublin Complex & Adaptive Systems Lab, School of Computer Science & Informatics Dublin Ireland
    • Leonardo Vanneschi, University of Milano-Bicocca Milan Italy
    • Steven Gustafson, GE Global Research Niskayuna NY USA
    • Wolfgang Banzhaf, Memorial University of Newfoundland St. John’s NL Canada

ICPR 2010 – Contest: Extended Deadline May, 26

Call for Contest Participation – Classifier domains of competence: The landscape contest (ICPR 2010) Classifier domains of competence: The landscape contest is a research competition aimed at finding out the […]

Call for Contest Participation – Classifier domains of competence: The landscape contest (ICPR 2010)

Classifier domains of competence: The landscape contest is a research competition aimed at finding out the relation between data complexity and the performance of learners. Comparing your techniques to those of other participants on targeted-complexity problems may contribute to enrich our understanding of the behavior of machine learning techniques and open further research lines.

The contest will take place on August 22, during the 20th International Conference on Pattern Recognition (ICPR 2010) at Istanbul, Turkey.

We encourage everyone to participate and share with us your work! For further details about dates and submission, please see http://www.salle.url.edu/ICPR10Contest/.

SCOPE OF THE CONTEST

The landscape contest involves the running and evaluation of classifier systems over synthetic data sets. Over the last two decades, the pattern recognition and machine learning communities have developed many supervised learning techniques. Nevertheless, the competitiveness of such techniques has always been claimed over a small and repetitive set of problems. This contest provides a new and configurable testing framework, reliable enough to test the robustness of each technique and detect its limitations.

INSTRUCTION FOR PARTICIPANTS

Contest participants are allowed to use any type of technique. However, we highly encourage and appreciate the use of novel algorithms.

Participants are required to submit the results by email to the organizers.
Submission e-mail: nmacia@salle.url.edu
Meet the submission deadline: Wednesday May 26, 2010

The contest is divided into two phases: (1) offline test and (2) live test. For the offline test, participants should run their algorithms over two sets of problems, S1 and S2. However, the real competition, the live test, will take place during the conference. Two more collections of problems, S3 and S4, will be presented.

S1: Collection of data sets spread along the complexity space to train the learner. All the instances will be duly labeled.

S2: Collection of data sets spread along the complexity space with no class labeling to test the learner performance.

S3: Collection of data sets with no class labeling, like S2 to be run for a limited period of time.

S4: Collection of data sets with no class labeling covering specific regions of the complexity space to determine the neighborhood dominance.

For the offline test, the results report consists of:

1. Labeling the data sets of the collection S2.

The procedure is the following:

  1. Train the learner using Dn-trn.arff in S1.
  2. Provide the rate of the correctly classified instances over a 10-fold cross validation.
  3. Label the corresponding data set Dn-tst.arff in S2.
  4. Store the n models generated for each data set to perform the live contest on August 22. Be ready to load them on this day.

2. Describing the techniques used.

A brief summary (1~2 pages) of the machine learning technique/s used in the experiments must be submitted. We expect details such as the learning paradigm, configuration parameters, strength and limitations, and computational cost.

IMPORTANT DATES

* May 26, 2010: Deadline for submission of the results and technical report

* May 29, 2010: Notification of participation

* Aug 22, 2010: Release of S3 and S4

* Aug 22, 2010: ICPR 2010 – Interactive Session


CONTACT DETAILS

Dr. Tin Kam Ho – tkh at research.bell-labs.com
Núria Macià – nmacia at salle.url.edu
Prof. Albert Orriols Puig – aorriols at salle.url.edu
Prof. Ester Bernadó Mansilla – esterb at salle.url.edu

ICPR 2010 – Contest: Extended Deadline May, 26

Call for Contest Participation – Classifier domains of competence: The landscape contest (ICPR 2010) Classifier domains of competence: The landscape contest is a research competition aimed at finding out the relation between data complexity and the performance of learners. Comparing your techniques to those of other participants on targeted-complexity problems may contribute to enrich our […]

Call for Contest Participation – Classifier domains of competence: The landscape contest (ICPR 2010)

Classifier domains of competence: The landscape contest is a research competition aimed at finding out the relation between data complexity and the performance of learners. Comparing your techniques to those of other participants on targeted-complexity problems may contribute to enrich our understanding of the behavior of machine learning techniques and open further research lines.

The contest will take place on August 22, during the 20th International Conference on Pattern Recognition (ICPR 2010) at Istanbul, Turkey.

We encourage everyone to participate and share with us your work! For further details about dates and submission, please see http://www.salle.url.edu/ICPR10Contest/.

SCOPE OF THE CONTEST

The landscape contest involves the running and evaluation of classifier systems over synthetic data sets. Over the last two decades, the pattern recognition and machine learning communities have developed many supervised learning techniques. Nevertheless, the competitiveness of such techniques has always been claimed over a small and repetitive set of problems. This contest provides a new and configurable testing framework, reliable enough to test the robustness of each technique and detect its limitations.

INSTRUCTION FOR PARTICIPANTS

Contest participants are allowed to use any type of technique. However, we highly encourage and appreciate the use of novel algorithms.

Participants are required to submit the results by email to the organizers.
Submission e-mail: nmacia@salle.url.edu
Meet the submission deadline: Wednesday May 26, 2010

The contest is divided into two phases: (1) offline test and (2) live test. For the offline test, participants should run their algorithms over two sets of problems, S1 and S2. However, the real competition, the live test, will take place during the conference. Two more collections of problems, S3 and S4, will be presented.

S1: Collection of data sets spread along the complexity space to train the learner. All the instances will be duly labeled.

S2: Collection of data sets spread along the complexity space with no class labeling to test the learner performance.

S3: Collection of data sets with no class labeling, like S2 to be run for a limited period of time.

S4: Collection of data sets with no class labeling covering specific regions of the complexity space to determine the neighborhood dominance.

For the offline test, the results report consists of:

1. Labeling the data sets of the collection S2.

The procedure is the following:

  1. Train the learner using Dn-trn.arff in S1.
  2. Provide the rate of the correctly classified instances over a 10-fold cross validation.
  3. Label the corresponding data set Dn-tst.arff in S2.
  4. Store the n models generated for each data set to perform the live contest on August 22. Be ready to load them on this day.

2. Describing the techniques used.

A brief summary (1~2 pages) of the machine learning technique/s used in the experiments must be submitted. We expect details such as the learning paradigm, configuration parameters, strength and limitations, and computational cost.

IMPORTANT DATES

* May 26, 2010: Deadline for submission of the results and technical report

* May 29, 2010: Notification of participation

* Aug 22, 2010: Release of S3 and S4

* Aug 22, 2010: ICPR 2010 – Interactive Session


CONTACT DETAILS

Dr. Tin Kam Ho – tkh at research.bell-labs.com
Núria Macià – nmacia at salle.url.edu
Prof. Albert Orriols Puig – aorriols at salle.url.edu
Prof. Ester Bernadó Mansilla – esterb at salle.url.edu

Demolition derby, another GECCO-2010 competition

http://www.flickr.com/photos/jrandallc/ / CC BY-SA 2.0
GECCO-2010 has yet another exciting competition! The fourth competition at GECCO-2010 is called Demolition Derby. As the name suggests, the goal of Demolition Derby is simple: wreck all opponent cars by crashing into them without getting wrecked yourself. The submission deadline for this competition is June 27th 2010. More information can […]

GECCO-2010 has yet another exciting competition! The fourth competition at GECCO-2010 is called Demolition Derby. As the name suggests, the goal of Demolition Derby is simple: wreck all opponent cars by crashing into them without getting wrecked yourself. The submission deadline for this competition is June 27th 2010. More information can be found on GECCO-2010 Competitions page.

Besides the demolition derby, GECCO-2010 houses another three competitions, which we announced earlier:

  • Evolutionary Art Competition
  • GPUs for Genetic and Evolutionary Computation
  • 2010 Simulated Car Racing Championship

W. Brian Arthur to give a keynote at GECCO-2010

I am pleased to announce that one of the GECCO-2010 keynotes will be given by W. Brian Arthur. More on the speech below:
Title: Combinatorial evolution in technology and an algorithm this suggests
Abstract: Brian Arthur will talk about his new book, The Nature of Technology, which lays out an understanding of how technology comes into being […]

I am pleased to announce that one of the GECCO-2010 keynotes will be given by W. Brian Arthur. More on the speech below:

Title: Combinatorial evolution in technology and an algorithm this suggests

Abstract: Brian Arthur will talk about his new book, The Nature of Technology, which lays out an understanding of how technology comes into being and how it evolves. He will also talk about a new algorithm based on technological evolution that builds up families of technologies from ones that previously exist; and discuss how it compares with genetic algorithms.

Biosketch of the speaker: Brian Arthur´s background is in engineering and mathematics, but he is best known as an economist. From 1983 to 1996 he was Dean and Virginia Morrison Professor of Population Studies and Economics at Stanford. And from 1988 to 2004 he was Citibank Professor at the Santa Fe Institute. Arthur is well-known for his “theory of increasing returns”, which explains what happens when products that gain market share find it easier to gain further market share, and how such positive feedbacks lock markets in to the domination of one or two players. Arthur is also one of the pioneers of the science of complexity – the science of how patterns and structures self-organize. He directed the Santa Fe Institute´s first research program in 1988. He is the recipient of the International Schumpeter Prize in Economics, the inaugural Lagrange Prize in Complexity Science, and two honorary doctorates.

Rama Chellappa on face recognition

Last night our department hosted the 13th annual Spencer & Spencer lecture on face recognition presented by Rama Chellappa. Rama Chellappa is Minta Martin Professor of Engineering and the director of the Center for Automation Research at the University of Maryland, College Park. The lecture discussed the recent advances in face recognition, and I was […]

Last night our department hosted the 13th annual Spencer & Spencer lecture on face recognition presented by Rama Chellappa. Rama Chellappa is Minta Martin Professor of Engineering and the director of the Center for Automation Research at the University of Maryland, College Park. The lecture discussed the recent advances in face recognition, and I was quite impressed with some of the recent work.

Grammar-based Genetic Programming: a survey

Abstract  Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have
also become important as a method for formalizing constraints in Genetic Programming (GP). Practical gramma…

Abstract  

Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have
also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems
first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace
their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions
they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP,
showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development
of grammar-based GP systems, and conclude with a brief summary of the field.

  • Content Type Journal Article
  • Pages 365-396
  • DOI 10.1007/s10710-010-9109-y
  • Authors
    • Robert I. McKay, Seoul National University Structural Complexity Lab, School of Computer Science and Engineering Seoul Korea
    • Nguyen Xuan Hoai, Le Quy Don University Department of Computer Science Hanoi Vietnam
    • Peter Alexander Whigham, University of Otago Department of Information Science Dunedin NZ New Zealand
    • Yin Shan, Medicare Australia Canberra Australia
    • Michael O’Neill, University College Dublin Complex and Adaptive Systems Lab, School of Computer Science and Informatics Dublin Ireland

A definition for I-fuzzy partitions

Abstract  In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued
fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e….

Abstract  

In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued
fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e.g. fuzzy c-means), we define a method to construct them from a set of fuzzy clusters obtained from several executions of fuzzy c-means. From a practical point of view, the approach presented here tries to solve the difficulty of comparing the results
of fuzzy clustering methods and, in particular, the difficulty of finding the global optimal.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0605-z
  • Authors
    • Vicenç Torra, CSIC, Spanish Council for Scientific Research IIIA, Institut d’Investigació en Intel-ligència Artificial Campus de Bellaterra 08193 Bellaterra Catalonia Spain
    • Sadaaki Miyamoto, University of Tsukuba Department of Risk Engineering, School of Systems and Information Engineering Tsukuba Ibaraki 305-8573 Japan

Data analysis pipeline from laboratory to MP models

Abstract  A workflow for data analysis is introduced to synthesize flux regulation maps of a Metabolic P system from time series of
data observed in laboratory. The procedure is successfully tested on a significant case study, the photosynth…

Abstract  

A workflow for data analysis is introduced to synthesize flux regulation maps of a Metabolic P system from time series of
data observed in laboratory. The procedure is successfully tested on a significant case study, the photosynthetic phenomenon
called NPQ, which determines plant accommodation to environmental light. A previously introduced MP model of such a photosynthetic
process has been improved, by providing an MP system with a simpler regulative network that reproduces the observed behaviors
of the natural system. Two regression techniques were employed to find out the regulation maps, and interesting experimental
results came out in the context of their residual analysis for model validation.

  • Content Type Journal Article
  • Pages 55-76
  • DOI 10.1007/s11047-010-9200-6
  • Authors
    • Alberto Castellini, Computer Science Department, Verona University, Strada Le Grazie 15, 37134 Verona, Italy
    • Giuditta Franco, Computer Science Department, Verona University, Strada Le Grazie 15, 37134 Verona, Italy
    • Roberto Pagliarini, Computer Science Department, Verona University, Strada Le Grazie 15, 37134 Verona, Italy