Author: Community

GPEM 10(2) now available online

The second issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:

Incorporating characteristics of human creativity into an evolutionary art algorithm
by Steve DiPaola, Liane Gabora
Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis
by Stephan M. Winkler, Michael Affenzeller, Stefan Wagner
Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories
by Sara Silva, Ernesto Costa
A review of procedures to evolve quantum algorithms
by Adrian Gepp, Phil Stocks
Book Review: Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming
by Michael O’Neill

The second issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:

Incorporating characteristics of human creativity into an evolutionary art algorithm
by Steve DiPaola, Liane Gabora
Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis
by Stephan M. Winkler, Michael Affenzeller, Stefan Wagner
Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories
by Sara Silva, Ernesto Costa
A review of procedures to evolve quantum algorithms
by Adrian Gepp, Phil Stocks
Book Review: Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming
by Michael O’Neill

Website for The Art of Artificial Evolution

A nice website has been set up for The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, a book edited by Juan Romero and Penousal Machado that was reviewed by Jeroen Eggermont in GPEM 10(1).

A nice website has been set up for The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, a book edited by Juan Romero and Penousal Machado that was reviewed by Jeroen Eggermont in GPEM 10(1).

A three-step decomposition method for the evolutionary design of sequential logic circuits

Abstract  Evolvable hardware (EHW) refers to an automatic circuit design approach, which employs evolutionary algorithms (EAs) to generate
the configurations of the programmable devices. The scalability is one of the main obstacles preventin…

Abstract  Evolvable hardware (EHW) refers to an automatic circuit design approach, which employs evolutionary algorithms (EAs) to generate
the configurations of the programmable devices. The scalability is one of the main obstacles preventing EHW from being applied
to real-world applications. Several techniques have been proposed to overcome the scalability problem. One of them is to decompose
the whole circuit into several small evolvable sub-circuits. However, current techniques for scalability are mainly used to
evolve combinational logic circuits. In this paper, in order to decompose a sequential logic circuit, the state decomposition,
output decomposition and input decomposition are united as a three-step decomposition method (3SD). A novel extrinsic EHW
system, namely 3SD–ES, which combines the 3SD method with the (μ, λ) ES (evolution strategy), is proposed, and is used for the evolutionary designing of larger sequential logic circuits. The
proposed extrinsic EHW system is tested extensively on sequential logic circuits taken from the Microelectronics Center of
North Carolina (MCNC) benchmark library. The results demonstrate that 3SD–ES has much better performance in terms of scalability.
It enables the evolutionary designing of larger sequential circuits than have ever been evolved before.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-009-9083-4
  • Authors
    • Houjun Liang, University of Science and Technology of China Nature Inspired Computation and Applications Laboratory (NICAL), Department of Computer Science and Technology 230027 Hefei Anhui China
    • Wenjian Luo, University of Science and Technology of China Nature Inspired Computation and Applications Laboratory (NICAL), Department of Computer Science and Technology 230027 Hefei Anhui China
    • Xufa Wang, University of Science and Technology of China Nature Inspired Computation and Applications Laboratory (NICAL), Department of Computer Science and Technology 230027 Hefei Anhui China

LCS & GBML Central: Community resource is now Online

LCSweb was designed to allow researchers and those seeking to use Learning Classifier Systems within applications access to material on LCS and discussion between members of the LCS community. The site served this community since its was started by Alwyn Barry in 1997. Enhanced and maintained later by Jan Drugowitsch, LCSweb became a valuable community […]

LCSweb was designed to allow researchers and those seeking to use Learning Classifier Systems within applications access to material on LCS and discussion between members of the LCS community. The site served this community since its was started by Alwyn Barry in 1997. Enhanced and maintained later by Jan Drugowitsch, LCSweb became a valuable community resource. The site was completely community-driven and allowed members to contribute to the content of the site and keeping it up to date. Later on in 2005, I started “LCS and other GBML” Blog to cover a gap providing information information regarding the International Workshop on Learning Classifier Systems (IWLCS), the collection of LCS Books available, and GBML related news.Some of you may have realized that after Jan’s move to Rochester and Alwyn’s retirement from research activities, LCSweb has vanished. Will Browne took on himself to take LCSweb to Reading, but technical circumstances have made that move rocky despite his best efforts. Jan and Will however still have a local copy of LCSweb contents. After talking to Jan and Will, I proposed to merge LCSweb with the LCS and other GBML blog, and host the new site at NCSA where dedicated resources has been made available. Jan and Will agreed with the idea.We are happy to announce that the merged site (still under the update cycle) can be reached at http://lcs-gbml.ncsa.uiuc.edu. More information about the process can be found here or at there LCS & GBML Central site.

Related Posts

    MID-CBR meeting

    On March 19th and 20th 2009 took place the annual meeting of the MID-CBR (Marco Integrador para el Desarrollo de Sistemas de Razonamiento Basado en Casos, TIN2006-15140-C03) a coordinated project by the Instituto de Investigación de Inteligencia Artificial (IIIA-CSIC; Main Researcher: Dr. Enric Plaza), the Universidad Complutense de Madrid (GAIA-UCM; Main Researcher: Dra. Belén […]

    On March 19th and 20th 2009 took place the annual meeting of the MID-CBR (Marco Integrador para el Desarrollo de Sistemas de Razonamiento Basado en Casos, TIN2006-15140-C03) a coordinated project by the Instituto de Investigación de Inteligencia Artificial (IIIA-CSIC; Main Researcher: Dr. Enric Plaza), the Universidad Complutense de Madrid (GAIA-UCM; Main Researcher: Dra. Belén […]

    XCSLib: The XCS Classifier System Library

    for IlliGAL Report No. 2009005:
    The XCS Library (XCSLib) is an open source C++ library for
    genetics-based machine learning and learning classifier systems. It
    provides (i) several reusable components that can be employed to design
    new learning paradigm…

    for IlliGAL Report No. 2009005:

    The XCS Library (XCSLib) is an open source C++ library for
    genetics-based machine learning and learning classifier systems. It
    provides (i) several reusable components that can be employed to design
    new learning paradigms inspired to the learning classifier system
    principles; and (ii) the implementation of two well-known and widely
    used models of learning classifier systems.

    Related Posts

      Prof. Chua interviewed by “El País”

      Prof. León Chua visited Barcelona to preside over the examining committee of one of our former members and current collaborators, Giovanni Pazienza. On this visit, he had time to share his main discovery with us in the talk: “Memristor: 37 years later”.
      Through an unusual presentation, overhead transparencies, amusing jokes, technological explanations hidden in […]

      Prof. León Chua visited Barcelona to preside over the examining committee of one of our former members and current collaborators, Giovanni Pazienza. On this visit, he had time to share his main discovery with us in the talk: “Memristor: 37 years later”.
      Through an unusual presentation, overhead transparencies, amusing jokes, technological explanations hidden in […]

      Evolutionary design of Evolutionary Algorithms

      Abstract  Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult
      task. This is why we have to find other manners to construct algorithms that perform very well on some proble…

      Abstract  Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult
      task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility
      (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for
      solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here.
      The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular
      problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical
      experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known
      benchmarking problems.

      • Content Type Journal Article
      • Category Original Paper
      • DOI 10.1007/s10710-009-9081-6
      • Authors
        • Laura Dioşan, Babeş-Bolyai University Department of Computer Science, Faculty of Mathematics and Computer Science Kogalniceanu 1 Cluj-Napoca 400084 Romania
        • Mihai Oltean, Babeş-Bolyai University Department of Computer Science, Faculty of Mathematics and Computer Science Kogalniceanu 1 Cluj-Napoca 400084 Romania

      Medical applications as a growth area for genetic and evolutionary computing

      Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

      Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
      I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

      Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

      Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
      I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

      Semantic analysis of program initialisation in genetic programming

      Abstract  Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly,
      and hard, because these random combinations of syntax do not always produce random and diverse…

      Abstract  Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly,
      and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this
      paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic
      program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create
      four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming
      problems. We present results to show that varying the choice and design of program initialisation can dramatically influence
      the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects
      on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.

      • Content Type Journal Article
      • Category Original Paper
      • DOI 10.1007/s10710-009-9082-5
      • Authors
        • Lawrence Beadle, University of Kent Computing Laboratory Canterbury CT2 7NF UK
        • Colin G. Johnson, University of Kent Computing Laboratory Canterbury CT2 7NF UK