Modelling Genetic Programming as a Simple Sampling Algorithm

  • White D
  • Fowler B
  • Banzhaf W
  • et al.
Citations of this article
Mendeley users who have this article in their library.
Get full text


This chapter proposes a new model of tree-based Genetic Programming (GP) as a simple sampling algorithm that samples minimal schemata (subsets of the solution space) described by a single concrete node at a single position in the expression tree. We show that GP explores these schemata in the same way across three benchmarks, rapidly converging the population to a specific function at each position throughout the upper layers of the expression tree. This convergence is driven by covariance between membership of a simple schema and rank fitness. We model this process using Prices theorem \cite{price:nature} and provide empirical evidence to support our model. The chapter closes with an outline of a modification of the standard GP algorithm that reinforces this bias by converging populations to fit schemata in an accelerated way.




White, D. R., Fowler, B., Banzhaf, W., & Barr, E. T. (2020). Modelling Genetic Programming as a Simple Sampling Algorithm (pp. 367–381).

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free