Algorithms, games, and evolution

作者：Chastain Erick; Livnat Adi; Papadimitriou Christos^*; Vazirani Umesh

来源：Proceedings of the National Academy of Sciences, 2014, 111(29): 10620-10623.

DOI：10.1073/pnas.1406556111

• coordination games
• learning algorithms

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.

• 出版日期2014-7-22