Genetic programming: optimal population sizes for varying complexity problems

Authors:
Alan Piszcz;Terence Soule
Affiliations:
University of Idaho, Moscow, ID;University of Idaho, Moscow, ID
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 0
Cited 4

Genetic programming: profiling reasonable parameter value windows with varying problem difficulty

International Journal of Innovative Computing and Applications
A survey and taxonomy of performance improvement of canonical genetic programming

Knowledge and Information Systems
A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models

Computers and Operations Research
Experimental study of the population parameters settings in cooperative multi-agent system solving instances of the VRP

Transactions on Computational Collective Intelligence IX

Quantified Score

Hi-index	0.00

Visualization

Abstract

The population size in evolutionary computation is a significant parameter affecting computational effort and the ability to successfully evolve solutions. We find that population size sensitivity - how much a genetic program's efficiency varies with population size - is correlated with problem complexity. An analysis of population sizes was conducted using a unimodal, bimodal and a multi-modal problem with varying levels of difficulty. Specifically we show that a unimodal and bimodal and multimodal problems exhibit an increased sensitivity to population size with increasing levels of difficulty. We demonstrate that as problem complexity increases, determination of the optimal population size becomes more difficult. Conversely, the less complex a problem is the more sensitive the genetic program's efficiency is to population size.