[Seminars] PSB event reminder

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Calendar Name: seminars
Scheduled for: Wednesday, October 2 2013, 14:00 - 15:30
Event text:    Prof Yves Van de Peer
	       
	       VIB, Dept. of Plant Systems Biology
	       Ghent University
	       
	       Gent
	       BELGIUM
Details:       "I, robot.  Using novel bio-inspired principles to
	       improve adaptability of evolutionary robots in
	       dynamically changing environments"
	       
	       ABSTRACT
	       One of the important challenges in the field of
	       evolutionary robotics is the development of systems that
	       can adapt to changing environments. However, adaptation
	       to a changing or fluctuating environment, characteristic
	       of a complex adaptive system (CAS), is not
	       straightforward and usually requires a dynamic and
	       preferentially robust solution.	In this study, we
	       explore the adaptive potential of simulated robots that
	       contain a genomic encoding of a gene regulatory network
	       (GRN).  This hard-wired (static) genomic encoding is
	       combined with an agent-based system, which transduces
	       environmental cues into phenotypic behaviour. Using a
	       simulation framework that mimics a dynamically changing
	       environment, we demonstrate that the combination of a
	       genomic encoding of the GRN and an agent based system
	       has several advantages over more traditional approaches.
		Indeed, separating the static from the conditionally
	       active part of the network allows changes to affect
	       parts of the static network, generating diversity that
	       might later prove beneficial in a different environment,
	       while not affecting the part of the GRN that is
	       currently active.  Furthermore, beneficial network
	       re-wiring in the static genome provides a memory imprint
	       that allows faster re-adaptation to a situation
	       encountered previously.	Applying these
	       evolutionary-based principles increases the potential to
	       evolve and adapt in a non-stable environment.

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