NDLI: Evolvability of Real Functions

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Evolvability of Real Functions

Content Provider	ACM Digital Library
Author	Valiant, Paul
Copyright Year	2014
Abstract	We formulate a notion of evolvability for functions with domain and range that are real-valued vectors, a compelling way of expressing many natural biological processes. We show that linear and fixed-degree polynomial functions are evolvable in the following dually-robust sense: There is a single evolution algorithm that, for all convex loss functions, converges for all distributions. It is possible that such dually-robust results can be achieved by simpler and more-natural evolution algorithms. Towards this end, we introduce a simple and natural algorithm that we call wide-scale random noise and prove a corresponding result for the $L_{2}$ metric. We conjecture that the algorithm works for a more general class of metrics.
Starting Page	1
Ending Page	19
Page Count	19
File Format	PDF
ISSN	19423454
e-ISSN	19423462
DOI	10.1145/2633598
Volume Number	6
Issue Number	3
Journal	ACM Transactions on Computation Theory (TOCT)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolvability Optimization
Content Type	Text
Resource Type	Article
Subject	Computational Theory and Mathematics Theoretical Computer Science

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