Created by W.Langdon from gp-bibliography.bib Revision:1.7954
In this study, we explore the evaluation time of GP models as a measure of their complexity; we define the evaluation time as the time taken to evaluate a model over some data. We demonstrate that the evaluation time reflects both a model's size and its composition; also, we show how to measure the evaluation time reliably.
To validate our proposal, we leverage four well-known methods to size-control but to control evaluation times instead of the tree sizes; we thus compare size-control with time-control. The results show that time-control with a nuanced notion of complexity produces more accurate models on 17 out of 20 problem scenarios. Even when the models have slightly greater times and sizes, time-control counterbalances via superior accuracy on both training and test data. The paper also argues that time-control can differentiate functional complexity even better in an identically-sized population. To facilitate this, the paper proposes Fixed Length Initialisation (FLI) that creates an identically-sized but functionally-diverse population. The results show that while FLI particularly suits time-control, it also generally improves the performance of size-control. Overall, the paper poses evaluation-time as a viable alternative to tree sizes to measure complexity in GP.",
Genetic Programming entries for Aliyu Sani Sambo R Muhammad Atif Azad Yevgeniya Kovalchuk Vivek Padmanaabhan Indramohan Hanifa Shah