Diverse forms of selection in evolution and computer science

Abstract

The astonishing diversity of complex adaptations that we see in the living world has been produced by natural selection, over ∼3.5 billion years of evolution. Information from the largely random survival and reproduction of past organisms has accumulated to produce genomes that are precisely fitted to diverse environments. In PNAS, Chastain et al. (1) show that natural selection on freely recombining populations is equivalent to the multiplicative weights update algorithm (MWUA), an efficient optimization algorithm that has been discovered many times in computer science, statistics, and economics. Whether this equivalence explains the extraordinary effectiveness of natural selection, or conversely, of artificial algorithms, depends on one's perspective. Perhaps surprisingly, theoretical results in population genetics and in computer science look quite different, even when they deal with essentially the same questions. Thus, the equivalence identified by Chastain et al. (1) allows these different results to be transferred between fields in both directions. In each generation of selection, the frequency of each type is simply multiplied by its relative fitness, which corresponds precisely to MWUA. Imagine a panel of financial experts whose advice performs in an arbitrary way over time. In each step, the probability of choosing one or other expert is updated in proportion to their performance. Remarkably, provided that the updates are gradual, the ultimate performance of the algorithm is guaranteed to be close to that of the expert with the best total performance, or equivalently, to that of the allele with the highest total fitness, summed over generations. This can be seen as a justification for the Darwinian view that gradual evolution is most effective and as quantifying the performance of natural selection in a randomly fluctuating environment. The MWUA can be represented by a simple maximization principle (Fig. 1 … [↵][1]1To whom correspondence should be addressed. Email: Nick.Barton{at}ist.ac.at. [1]: #xref-corresp-1-1