English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We show that evolutionary computation can be implemented as standardMarkov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms'can be constructed that are reversible Markov chains that satisfy detailedbalance; it follows that the stationary distribution of populations is a Gibbsdistribution in a simple factorised form. For some standard and popularnonparametric probability models, we exhibit Gibbs-sampling procedures that areplausible genetic algorithms. At mutation-selection equilibrium, a populationof genomes is analogous to a sample from a Bayesian posterior, and the genomesare analogous to latent variables. We suggest this is a general, tractable, andinsightful formulation of evolutionary computation in terms of standard machinelearning concepts and techniques.  In addition, we show that evolutionary processes in which selection acts bydifferences in fecundity are not reversible, and also that it is not possibleto construct reversible evolutionary models in which each child is produced byonly two parents.

Sex as Gibbs Sampling: a probability model of evolution