Title	Phenotype evolution as optimization
Creator	Otwinowski, Jakub
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-08-21T11:39
Description	Biological evolution can be described as a population climbing a fitness landscape, and has inspired a variety of derivative-free optimization algorithms. Here we describe how phenotype evolution has sophisticated optimization properties. In particular, natural selection approximates second order gradient descent (Newton's method), and recombination is efficient in generating diversity. We use these insights to design a new type of derivative-free optimization algorithm for continuous problems.
Extent	44.0 minutes
Subject	Mathematics; Statistical mechanics, structure of matter; Biology and other natural sciences; Mathematical biology
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: MPI for Dynamics and Self-organization
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2020-02-18
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0388653
URI	http://hdl.handle.net/2429/73557
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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