Multi-objective competitive coevolution for efficient GP classifier problem decomposition

A novel approach to the classification of large and unbalanced multi-class data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the genetic programming (GP) paradigm. A cooperative coevolutionary training environment that employs multi-objective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardware-specific speedups. Moreover, a key departure from the canonical GP approach to classification is utilized in which the output of GP is expressed in terms of a non-binary, local membership function (e.g. a Gaussian), where it is no longer necessary for an expression to represent an entire class. Decomposition is then achieved through reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers rather than by a single ´super´ individual.