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

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Efficiency Enhancement of Estimation of Distribution Algorithms

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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

New issue of SIGEVOlution Newsletter is online

SIGEVOlution Volume 3 Issue 1 is now available online at http://www.sigevolution.org. SIGEVOlution is the newsletter of ACM SIGEVO, the ACM Special Interest Group for Genetic and Evolutionary Computation. See the announcement at IlliGAL Blogging.

SIGEVOlution Volume 3 Issue 1 is now available online at http://www.sigevolution.org. SIGEVOlution is the newsletter of ACM SIGEVO, the ACM Special Interest Group for Genetic and Evolutionary Computation. See the announcement at IlliGAL Blogging.

Tina Yu, David Davis, Cem Baydar, Rajkumar Roy (eds): Evolutionary Computation in Practice: Studies in Computational Intelligence

Tina Yu, David Davis, Cem Baydar, Rajkumar Roy (eds): Evolutionary Computation in Practice: Studies in Computational Intelligence
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9068-8Authors
Larry M. Deschaine, Science Applic…

Tina Yu, David Davis, Cem Baydar, Rajkumar Roy (eds): Evolutionary Computation in Practice: Studies in Computational Intelligence

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9068-8
  • Authors
    • Larry M. Deschaine, Science Applications International Corporation Aiken SC USA

A parallel immune optimization algorithm for numeric function optimization

Abstract  Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization
problems where such solutions are often difficult to determine by traditional techniques. This arti…

Abstract  Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization
problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel
suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA
is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations
that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation.
In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated
by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations
and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with
other conventional optimization approaches.

  • Content Type Journal Article
  • DOI 10.1007/s12065-008-0014-8
  • Authors
    • Henry Y. K. Lau, The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Pokfulam Road Hong Kong, People’s Republic of China
    • Wilburn W. P. Tsang, The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Pokfulam Road Hong Kong, People’s Republic of China

Design and Analysis of Learning Classifier Systems: A Probabilistic Approach

The book Design and Analysis of Learning Classifier Systems: A Probabilistic Approach by Jan Drugowitsch presents a machine learning approach to Learning Classifier Systems. In the author’s own words:

This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question “What is an LCS supposed to learn?”. Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book  for illustrative purposes  closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework.

Scaling of program functionality

Abstract  The distribution of fitness values (landscapes) of programs tends to a limit as the programs get bigger. We use Markov chain
convergence theorems to give general upper bounds on the length of programs needed for convergence. How bi…

Abstract  The distribution of fitness values (landscapes) of programs tends to a limit as the programs get bigger. We use Markov chain
convergence theorems to give general upper bounds on the length of programs needed for convergence. How big programs need
to be to approach the limit depends on the type of the computer they run on. We give bounds (exponential in N, N log N and smaller) for five computer models: any, average or amorphous or random, cyclic, bit flip and four functions (AND, NAND,
OR and NOR). Programs can be treated as lookup tables which map between their inputs and their outputs. Using this we prove
similar convergence results for the distribution of functions implemented by linear computer programs. We show most functions
are constants and the remainder are mostly parsimonious. The effect of ad-hoc rules on genetic programming (GP) are described
and new heuristics are proposed. We give bounds on how long programs need to be before the distribution of their functionality
is close to its limiting distribution, both in general and for average computers. The computational importance of destroying
information is discussed with respect to reversible and quantum computers. Mutation randomizes a genetic algorithm population
in


\frac14(l+1)(log (l)+4)

generations. Results for average computers and a model like genetic programming are confirmed experimentally.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9065-y
  • Authors
    • W. B. Langdon, University of Essex Mathematical and Biological Sciences and Computing and Electronic Systems Essex CO4 3SQ UK

Coevolutionary bid-based genetic programming for problem decomposition in classification

Abstract  In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains
such as classification. This represents a significant departure from models where each individual constr…

Abstract  In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains
such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome
map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast,
the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated
with a single discrete action and the individual with the maximum bid ‘wins’ the right to suggest its action. Thus, the number
of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed
to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming
methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with
learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both
of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance
assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based
model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support
Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9067-9
  • Authors
    • Peter Lichodzijewski, Dalhousie University Faculty of Computer Science 6050 University Ave. Halifax NS Canada B3H 1W5
    • Malcolm I. Heywood, Dalhousie University Faculty of Computer Science 6050 University Ave. Halifax NS Canada B3H 1W5