Kumara Sastry

Population sizing for genetic programming based upon decision making

Sastry, K., O’Reilly, U.-M., Goldberg, D. E. (2004). Genetic Programming Theory and Practice II. 49—66. [Full paper - PDF] [Full paper - PS] [Presentation slides].

Abstract:
This paper derives a population sizing relationship for genetic programming (GP). Following the population-sizing derivation for genetic algorithms in Goldberg, Deb, and Clark (1992), it considers building block decision making as a key facet. The analysis yields a GP-unique relationship because it has to account for bloat and for the fact that GP solutions often use subsolutions multiple times. The population-sizing relationship depends upon tree size, solution complexity, problem difficulty and building block expression probability. The relationship is used to analyze and empirically investigate population sizing for three model GP problems named ORDER, ON-OFF and LOUD. These problems exhibit bloat to differing extents and differ in whether their solutions require the use of a building block multiple times.

Related Posts:

• Genetic algorithms, efficiency enhancement, and deciding well with fitness functions with differing variances
• Optimal sampling and speed-up for genetic algorithms on the sampled OneMax problem
• Scalability of selectorecombinative genetic algorithms for problems with tight linkage
• Building-block supply in genetic programming
• How well does a single-point crossover mix building blocks with tight linkage?

Comments are closed.