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Adaptation by Cumulative Selection

Rudy Arthur

TL;DR

This paper aims to provide an overarching theoretical framework which encompasses standard Darwinian evolution as well as other processes of adaptation and a general `recipe' for cumulative selection to occur.

Abstract

Biological systems like long-lived clonal organisms, holobionts and clades challenge traditional evolutionary thinking since they adapt without populations or reproduction. This paper aims to provide an overarching theoretical framework which encompasses standard Darwinian evolution as well as other processes of adaptation. This framework is cumulative selection and I provide a general `recipe' for it to occur. Lewontin's recipe for evolution by natural selection is shown to be a particular example of cumulative selection, but not the only one. Similarly, reproduction, inheritance and populations are just one way to perform cumulative selection. I discuss several other examples of cumulative selection including clonal organisms, dioecious populations, Gaia and neural networks.

Adaptation by Cumulative Selection

TL;DR

This paper aims to provide an overarching theoretical framework which encompasses standard Darwinian evolution as well as other processes of adaptation and a general `recipe' for cumulative selection to occur.

Abstract

Biological systems like long-lived clonal organisms, holobionts and clades challenge traditional evolutionary thinking since they adapt without populations or reproduction. This paper aims to provide an overarching theoretical framework which encompasses standard Darwinian evolution as well as other processes of adaptation. This framework is cumulative selection and I provide a general `recipe' for it to occur. Lewontin's recipe for evolution by natural selection is shown to be a particular example of cumulative selection, but not the only one. Similarly, reproduction, inheritance and populations are just one way to perform cumulative selection. I discuss several other examples of cumulative selection including clonal organisms, dioecious populations, Gaia and neural networks.
Paper Structure (22 sections, 43 equations, 2 figures)

This paper contains 22 sections, 43 equations, 2 figures.

Figures (2)

  • Figure 1: Simulations of the cumulative search process for $N=1$ compared to the analytical results above. Using $\nu=0, \mu=0.1, b=4$. Each point averages over 200 searches.
  • Figure 2: Simulations of the cumulative search process for $N>1$ using $\nu=0, \mu=0.1, b=4$. Each point averages 200 searches.