IEEE Computer Society announcement: April 1999

[Paul L. Rinaldo]

Gang,

FYI,

Paul

>To: ieee-announce@rstcorp.com
>From: ieee-announce@rstcorp.com
>Subject: IEEE Computer Society announcement: April 1999
>Date: Fri, 09 Apr 1999 13:07:41 -0500
>
>Dear IEEE member,
>
>We are pleased to announce the next IEEE Computer Society of Northern
>Virginia talk on Thursday, April 15th, 1999.
>
>The meeting will be held in the new Oracle facility in Reston, VA
>near the intersection of Sunset Hills Drive and the Reston Parkway
>(directions on Web page).
>
>We are pleased to resume our distinguished speaker series with a talk by
>Dr. Derek Linden on "Designing Like Mother Nature: An Introduction to
>Genetic Algorithms".
>
>Below are the details of the meeting. I'm looking forward to seeing you
>soon. As always, please check our Web page for the latest chapter
>events: http://www.rstcorp.com/ieee/.
>
>Regards,
>
>Anup K. Ghosh
>Vice Chair
>
>"Designing Like Mother Nature: an Introduction to Genetic Algorithms"
>
>by Dr. Derek Linden, Linden Innovation Research, LLC..
>
>When: Thursday, April 15th, 1999, 6:00 pm - 8:00 pm.
>
>Where: The new Oracle facility in Reston, VA, at the corner of Reston
>Parkway and Sunset Hills Road. Directions available at:
>http://www.rstcorp.com/ieee/directions.html#oracle.
>
>RSVP: please reserve your place so we have a head count for food and
>beverage. RSVP via: http://www.rstcorp.com/ieee/reserve.html.
>
>Cost: No charge for IEEE or IEEE CS members (please bring your
>membership card). All other guests must pay $4 at the door or $25 for
>all 1999 events. Food and beverage will be provided.
>
>Schedule:
>
>6:00 - 6:45: Social hour
>6:45 - 7:00: IEEE Business
>7:00 - 8:00: Guest speaker
>
>Abstract:
>
>ABSTRACT
>
> Researchers around the world are using Genetic Algorithms (GAs) to
>optimize many designs including antennas, aircraft engines, computer
>programs and even job schedules, with dramatic improvements in quality
>and design time. The GA is a probabilistic, iterative optimization
>strategy that mimics biological adaptation and evolution through mating
>and survival-of-the-fittest. It is very robust in difficult design
>spaces, finding good solutions to complex problems while avoiding local
>minima and exploring only a very small portion of the design space.
>Solutions found by GAs  also tend to be counter-intuitive, with
>unexpected parameters that often work better than designs created by
>engineers at great expense and time. The GA finds these solutions with
>little information about the problem and minimal involvement from the
>engineer--not even an initial guess. This talk will introduce GAs by
>exploring concepts and several examples of GA optimization.
>
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>