5 research outputs found
It is Time for New Perspectives on How to Fight Bloat in GP
The present and future of evolutionary algorithms depends on the proper use
of modern parallel and distributed computing infrastructures. Although still
sequential approaches dominate the landscape, available multi-core, many-core
and distributed systems will make users and researchers to more frequently
deploy parallel version of the algorithms. In such a scenario, new
possibilities arise regarding the time saved when parallel evaluation of
individuals are performed. And this time saving is particularly relevant in
Genetic Programming. This paper studies how evaluation time influences not only
time to solution in parallel/distributed systems, but may also affect size
evolution of individuals in the population, and eventually will reduce the
bloat phenomenon GP features. This paper considers time and space as two sides
of a single coin when devising a more natural method for fighting bloat. This
new perspective allows us to understand that new methods for bloat control can
be derived, and the first of such a method is described and tested.
Experimental data confirms the strength of the approach: using computing time
as a measure of individuals' complexity allows to control the growth in size of
genetic programming individuals