Incremental learning of collaborative classifier agents with new class acquisition - An incremental genetic algorithm approach

Abstract

A number of soft computing approaches, such as neural networks, evolutionary algorithms, and fuzzy logic, have been widely used for classifier agents to adaptively evolve solutions on classification problems. However, most work in the literature focuses on the learning ability of individual classifier agent. This paper explores incremental, collaborative learning in a multi-agent environment. We use genetic algorithm (GA) and incremental genetic algorithm (IGA) as the main techniques to evolve the rule set for classification, and employ new class acquisition as a typical example to illustrate the incremental, collaborative learning capability of classifier agents. Benchmark data sets are used to evaluate proposed approaches. The results show that GA and IGA can be successfully used for collaborative learning among classifier agents.