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2 Draft Papers: Swarm Search, Self-Regulated Pop. Size and Social Cognitive Maps

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  • Vitorino RAMOS
    Dear Colleagues: Two of my recent works can now be found online. hope u could enjoy them. best regards, vitorino ramos ... Varying the Population Size of
    Message 1 of 1 , Mar 3, 2005
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      Dear Colleagues:
      Two of my recent works can now be found online. hope u could enjoy them.
      best regards, vitorino ramos

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      Varying the Population Size of Artificial Foraging Swarms on Time Varying
      Landscapes,
      final draft submitted to ICCANN -05, International Conf. on Artificial
      Neural Networks,
      Springer-Verlag, LNCS series, Warsaw, Poland, Sep. 11-15, 2005.

      link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_59.html
      PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ICANN05.pdf

      ABSTRACT: Swarm Intelligence (SI) is the property of a system whereby the
      collective behaviors of (unsophisticated) entities interacting locally with
      their environment cause coherent functional global patterns to emerge. SI
      provides a basis with wich it is possible to explore collective (or
      distributed) problem solving without centralized control or the provision
      of a global model. In this paper we present a Swarm Search Algorithm with
      varying population of agents. The swarm is based on a previous model with
      fixed population which proved its effectiveness on several computation
      problems. We will show that the variation of the population size provides
      the swarm with mechanisms that improves its self-adaptability and causes
      the emergence of a more robust self-organized behavior, resulting in a
      higher efficiency on searching peaks and valleys over dynamic search
      landscapes represented here - for the purpose of different experiments - by
      several three-dimensional mathematical functions that suddenly change over
      time. We will also show that the present swarm, for each function,
      self-adapts towards an optimal population size, thus self-regulating.

      KEYWORDS: Swarm Intelligence and Perception, Dynamic Population Sizes,
      Self-Regulation, Social Cognitive Maps, Social Foraging, Self-Organization
      and Evolution, Distributed Search and Optimization.

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      Social Cognitive Maps, Swarm Collective Perception and Distributed Search
      on Dynamic
      Landscapes, CVRM-IST 127E-2005 technical report, final draft submitted to
      Brains, Minds & Media,
      Journal of New Media in Neural and Cognitive Science, NRW, Germany, 2005.

      link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html
      PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-BMM.pdf

      ABSTRACT: Swarm Intelligence (SI) is the property of a system whereby the
      collective behaviors of (unsophisticated) entities interacting locally with
      their environment cause coherent functional global patterns to emerge. SI
      provides a basis with which it is possible to explore collective (or
      distributed) problem solving without centralized control or the provision
      of a global model. To tackle the formation of a coherent social collective
      intelligence from individual behaviors, we discuss several concepts related
      to self-organization, stigmergy and social foraging in animals. Then, in a
      more abstract level we suggest and stress the role played not only by the
      environmental media as a driving force for societal learning, as well as by
      positive and negative feedbacks produced by the many interactions among
      agents. Finally, presenting a simple model based on the above features, we
      will address the collective adaptation of a social community to a cultural
      (environmental, contextual) or media informational dynamical landscape,
      represented here - for the purpose of different experiments - by several
      three-dimensional mathematical functions that suddenly change over time.
      Results indicate that the collective intelligence is able to cope and
      quickly adapt to unforeseen situations even when over the same cooperative
      foraging period, the community is requested to deal with two different and
      contradictory purposes.

      KEYWORDS: Swarm Intelligence and Perception, Social Cognitive Maps, Social
      Foraging, Self-Organization, Distributed Search and Optimization.

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