CLISDE: An Agent-Oriented Cladistic Island Genetic Algorithm

Research areas in cooperative multiagent learning have found success in using selection-based methods, such as Evolutionary Algorithms (EAs), in order for agents to measure their own progress within the system, guided by a fitness function. There have also been approaches which combine Genetic Algorithms (GAs) with supervised learning problems, such as decision trees. This research introduces a holonic agent-oriented implementation of a recursively nested Island genetic algorithm, called CLISDE, that builds a decision tree learner. The unique contribution of this paper is to show that Island genetic algorithms can be implemented in a holonic multiagent-based environment, and it can result in a model expressing self-similarity of the problem type and the architecture, achieved by integrating the learning algorithm of decision trees directly into the evolutionary process. The CLISDE GA and three known parallel GA models, namely the Island GA, Master-Slave GA and Hierarchical GA, were evaluated on a classification dataset and their performance metrics were compared. The results showed that the CLISDE GA produces a stronger decision tree predictor.