Predict the performance of GE with an ACO based machine learning algorithm
Gopinath Chennupati, Raja Muhammad Atif Azad, Conor Ryan · 2014
The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs and a significant percentage of runs can produce solutions of undesirable quality. These runs are a waste of computational resources, particularly in difficult problems where practitioners have time bound limitations in repeating runs. This paper proposes a completely novel approach, that of a Run Prediction Model (RPM) in which we identify and terminate evolutionary runs that are likely to produce low-quality solutions. This is justified with an Ant Colony Optimization (ACO) based classifier that learns from the early generations of a run and decides whether to continue or not.