It is Time for New Perspectives on How to Fight Bloat in GP
This addresses the bloat issue in Genetic Programming, which is a domain-specific problem for researchers and practitioners in evolutionary algorithms, but the approach is incremental as it builds on existing ideas with a new twist.
The paper tackles the problem of bloat (unnecessary growth) in Genetic Programming by proposing a new perspective that links evaluation time to individual complexity, showing that using computing time as a measure can control size growth in GP individuals.
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.