BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230921T162803
DTSTART;TZID=America/Detroit:20230926T120000
DTEND;TZID=America/Detroit:20230926T130000
SUMMARY:Workshop / Seminar:EEB Tuesday Lunch Seminar - Unsupervised Learning as an Analog for Evolvability in Genotype-Phenotype Maps
DESCRIPTION:This event is part of Ecology and Evolutionary Biology's weekly lunch series. \n\nAbout: The capability of an evolutionary substrate to generate novel phenotypic variation that is viable under mutation\, referred to as evolvability\, underpins the process of adaptive evolution. However\, evolutionary simulations using models with high-dimensional phenotypes often exhibit stunted evolvability in the absence of indirect genotype-phenotype mapping that facilitates coordinated changes over many phenotypic traits. If these genotype-phenotype maps bias toward phenotypic viability and maintain phenotypic diversity\, the resulting genetic search space will be lower-dimensional and less rugged\, making it more conducive to adaptive evolution. Such evolvability in genotype-phenotype maps shares significant conceptual overlap with unsupervised learning\, which extracts regularities and structure from unlabeled data that can enable lower-dimensional\, compact representations of complex data like images and text. Here\, we report a suite of benchmark fitness landscapes designed to facilitate head-to-head comparison of unsupervised learning techniques and evolved genotype-phenotype maps. This framework will contribute critical experimental rigor to ongoing efforts to harness unsupervised learning as a theoretical framework to understand evolvability. Exploration of unsupervised learning methods in engineering evolvable genotype-phenotype maps has great promise to benefit application-oriented evolutionary computation\, as well.
UID:111962-21828065@events.umich.edu
URL:https://events.umich.edu/event/111962
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:AEM Featured,Biology,Biosciences,Bsbsigns,department of ecology and evolutionary biology,ecology,Ecology & Biology,Ecology And Evolutionary Biology,eeb
LOCATION:Biological Sciences Building - 1010
CONTACT:
END:VEVENT
END:VCALENDAR