Selective Evolutionary Generation: A model for optimally efficient search in biology

This paper describes the biological principles underlying a recently proposed optimization technique, Selective Evolutionary Generation Systems (SEGS), and concludes a novel, fundamental result about the process of evolution in Nature. A systems-theoretic framework from the emerging field of self-reproducing systems is utilized in this work to illustrate the parallels between biological processes and SEGS. The SEGS technique is useful for tackling a generalization of the standard global optimization problem; the generalization depends on a parameter referred to as the level of selectivity, which restores traditional optimization when the parameter equals infinity. The SEGS technique has been shown to produce responsiveness efficiently, and to also be a generalization of both the canonical genetic algorithm with fitness proportional selection and the (1+1) evolutionary strategy. This paper explains how the SEGS technique models biological responsiveness and search, and the result is a Markov chain Monte Carlo method that has connections with statistical mechanics. The implication of the analysis is that natural evolution is an optimally efficient search process under certain technical conditions, which are often satisfied in Nature.