Author: Community

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9073-yAuthors
Michael O’Neill, School of Computer Science & Informatics, University …

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9073-y
  • Authors
    • Michael O’Neill, School of Computer Science & Informatics, University College Dublin Natural Computing Research & Applications Group, Complex and Adaptive Systems Laboratory Dublin Ireland

Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming

Abstract  In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear
singular systems using genetic programming (GP). The goal is to provide optimal control with reduced c…

Abstract  In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear
singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by
comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from
the GP method. We show that the GP approach to the problem is qualitatively better

in terms of accuracy. Numerical examples are provided to illustrate the proposed method.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9072-z
  • Authors
    • P. Balasubramaniam, Gandhigram Rural University Department of Mathematics Gandhigram 624 302 Tamilnadu India
    • A. Vincent Antony Kumar, PSNA College of Engineering and Technology Department of Computer Science and Applications Dindigul 624 622 Tamilnadu India

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9071-0Authors
Jeroen Eggermont, Leiden University Medical Center…

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9071-0
  • Authors
    • Jeroen Eggermont, Leiden University Medical Center Division of Image Processing, Department of Radiology Leiden The Netherlands

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9070-1Authors
Pierre Collet, Universite de Strasbourg FDBT-LSIIT Strasbourg France
…

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9070-1
  • Authors
    • Pierre Collet, Universite de Strasbourg FDBT-LSIIT Strasbourg France

MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars

Abstract  Recent work in the evolutionary computation field suggests that the implementation of the principles of modularity (functional
localization of functions), repetition (multiple use of the same sub-structure) and hierarchy (recursive…

Abstract  Recent work in the evolutionary computation field suggests that the implementation of the principles of modularity (functional
localization of functions), repetition (multiple use of the same sub-structure) and hierarchy (recursive composition of sub-structures)
could improve the evolvability of complex systems. The generation of neural networks through evolutionary algorithms should
in particular benefit from an adapted use of these notions. We have consequently developed modular encoding for neural networks
based on attribute grammars (MENNAG), a new encoding designed to generate the structure of neural networks and parameters
with evolutionary algorithms, while explicitly enabling these three above-mentioned principles. We expressed this encoding
in the formalism of attribute grammars in order to facilitate understanding and future modifications. It has been tested on
two preliminary benchmark problems: cart-pole control and robotic arm control, the latter being specifically designed to evaluate
the repetition capabilities of an encoding. We compared MENNAG to a direct encoding, ModNet, NEAT, a multi-layer perceptron
with a fixed structure and to reference controllers. Results show that MENNAG performs better than comparable encodings on
both problems, suggesting a promising potential for future applications.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0015-7
  • Authors
    • Jean-Baptiste Mouret, Université Pierre et Marie Curie, Paris 6 FRE 2507, ISIR, 4 place Jussieu 75005 Paris France
    • Stéphane Doncieux, Université Pierre et Marie Curie, Paris 6 FRE 2507, ISIR, 4 place Jussieu 75005 Paris France

OBUPM Presentations up

I want to thank everyone that participated in OBUPM-2008. The presentations were all very interesting and we had good participation from the audience. In particular I want to thank my fellow organizers, Martin Pelikan and Kumara Sastry. Without them, OBUPM would never have happened. Also thanks to Marc Ebner, the workshop chair, who had to […]

I want to thank everyone that participated in OBUPM-2008. The presentations were all very interesting and we had good participation from the audience. In particular I want to thank my fellow organizers, Martin Pelikan and Kumara Sastry. Without them, OBUPM would never have happened. Also thanks to Marc Ebner, the workshop chair, who had to deal with so many workshops at the same time.

Embedded below are four of the five presentations at OBUPM. When the last one is made available I will put it online also. These presentations can be downloaded from the OBUPM website here.

Thanks again and I hope to see you all at GECCO-2009!

Robust-Circuit-Sizing:-EDA-for-EDA

View SlideShare presentation or Upload your own. (tags: gecco obupm)
Real-Coded-Extended-Compact-Genetic-Algorithm-Based-on-Mixtures-of-Models

View SlideShare presentation or Upload your own. (tags: estimation-of-distributi… obupm)
An-Information-Geometry-Perspective-on-Estimation-of-Distribution-Algorithms:-Boundary-Analysis

View SlideShare presentation or Upload your own. (tags: obupm gecco)
Efficiency Enhancement of Estimation of Distribution Algorithms

View SlideShare presentation or Upload your own.

An improved representation for evolving programs

Abstract  A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining
their advantages. This combines the easy reproduction of the linear representation with the inherita…

Abstract  A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining
their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of
the tree representation by using fixed-length blocks of genes representing single program statements. This means that each
block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes
to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a
clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions
was evolved as an application of the new Genetic Program components. These functions have the common feature that they all
need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation
problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic
increase in the required evolution time.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9069-7
  • Authors
    • M. S. Withall, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK
    • C. J. Hinde, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK
    • R. G. Stone, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK