Author: Community

Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task

Abstract  This article presents results from an evaluation of the collective neuro-evolution (CONE) controller design method. CONE solves
collective behavior tasks, and increases task performance via facilitating emergent behavioral speciali…

Abstract  

This article presents results from an evaluation of the collective neuro-evolution (CONE) controller design method. CONE solves
collective behavior tasks, and increases task performance via facilitating emergent behavioral specialization. Emergent specialization
is guided by genotype and behavioral specialization difference metrics that regulate genotype recombination. CONE is comparatively
tested and evaluated with similar neuro-evolution methods in an extension of the multi-rover task, where behavioral specialization
is known to benefit task performance. The task is for multiple simulated autonomous vehicles (rovers) to maximize the detection
of points of interest (red rocks) in a virtual environment. Results indicate that CONE is appropriate for deriving sets of
specialized rover behaviors that complement each other such that a higher task performance, comparative to related controller
design methods, is attained in the multi-rover task.

  • Content Type Journal Article
  • DOI 10.1007/s12065-009-0034-z
  • Authors
    • G. S. Nitschke, Computational Intelligence Research Group, Department of Computer Science, University of Pretoria, Pretoria, 0002 South Africa
    • M. C. Schut, Computational Intelligence Group, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
    • A. E. Eiben, Computational Intelligence Group, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands

The combinatorics of modeling and analyzing biological systems

Abstract  The purpose of this paper is to present a strictly mathematical model for interaction networks, to address the question of
steady-state analysis, and to outline an approach for reconstructing models from experimental data. Our expo…

Abstract  

The purpose of this paper is to present a strictly mathematical model for interaction networks, to address the question of
steady-state analysis, and to outline an approach for reconstructing models from experimental data. Our expositions require
notations and basic results from discrete mathematics. Therefore, we also introduce some elementary background material from
this field.

  • Content Type Journal Article
  • Pages 655-681
  • DOI 10.1007/s11047-009-9165-5
  • Authors
    • Annegret K. Wagler, Magdeburg Center of Systems Biology (MaCS), Otto-von-Guericke Universität Magdeburg, Magdeburg, Germany
    • Robert Weismantel, Magdeburg Center of Systems Biology (MaCS), Otto-von-Guericke Universität Magdeburg, Magdeburg, Germany

Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications

Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications
Content Type Journal ArticlePages 123-125DOI 10.1007/s10710-009-9095-0Authors
Gisele L. Papp…

Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications

  • Content Type Journal Article
  • Pages 123-125
  • DOI 10.1007/s10710-009-9095-0
  • Authors
    • Gisele L. Pappa, Federal University of Minas Gerais (UFMG) Computer Science Department Av. Antônio Carlos, 6627, Pampulha Belo Horizonte MG Brazil

EvAg: a scalable peer-to-peer evolutionary algorithm

Abstract  This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping
protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes th…

Abstract  

This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping
protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently
suited for parallel execution in a peer-to-peer fashion which, in turn, offers great advantages when dealing with computationally
expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this
algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential
fashion. In particular, we analyze the behavior of several EAs on well-known deceptive trap functions with varying sizes and
levels of deceptiveness. The results show that the new EA requires smaller optimal population sizes and fewer fitness evaluations
to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some
cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude.

  • Content Type Journal Article
  • Pages 227-246
  • DOI 10.1007/s10710-009-9096-z
  • Authors
    • J. L. J. Laredo, University of Granada, ATC-ETSIT C. Periodista Daniel Saucedo Aranda 18071 Granada Spain
    • A. E. Eiben, Vrije Universiteit Amsterdam Department of Computer Science Amsterdam The Netherlands
    • M. van Steen, Vrije Universiteit Amsterdam Department of Computer Science Amsterdam The Netherlands
    • J. J. Merelo, University of Granada, ATC-ETSIT C. Periodista Daniel Saucedo Aranda 18071 Granada Spain