Parallel Genetic Algorithms and Machine Learning

Parallel computers offer massive potential in the domain of machine learning for investigating domains with domain theories more complex than those tractable with classical inductive algorithms. However to exploit the power of parallel computers it is necessary to develop an algorithm that works more efficiently in parallel than in serial. This paper outlines one approach to implementing a parallel machine learning system using Genetic Algorithms. We describe a framework that lets us learn an intensity feature map used as a basis of a classifier. We describe the limitations of our approach, and then outline possible areas of improvement for future work.