title: Algorithms, games, and evolution.
abstract: Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: "What algorithm could possibly achieve all this in a mere three and a half billion years?" In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution.
authors: Computer Science Department, Rutgers University, Piscataway, NJ 08854;
authors: Erick
authors: E
authors: Chastain
authors: Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061; and.
authors: Adi
authors: A
authors: Livnat
authors: Computer Science Division, University of California, Berkeley, CA, 94720 christos@cs.berkeley.edu.
authors: Christos
authors: C
authors: Papadimitriou
authors: Computer Science Division, University of California, Berkeley, CA, 94720.
authors: Umesh
authors: U
authors: Vazirani
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D005716:Game Theory; D005828:Genetics, Population; D006801:Humans
keywords: coordination games; learning algorithms