Modelling Genetic Programming as a Simple Sampling Algorithm

  • White D
  • Fowler B
  • Banzhaf W
  • et al.
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Abstract

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.

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White, D. R., Fowler, B., Banzhaf, W., & Barr, E. T. (2020). Modelling Genetic Programming as a Simple Sampling Algorithm (pp. 367–381). https://doi.org/10.1007/978-3-030-39958-0_18

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