Efficient Graph-Based Genetic Programing Representation with Multiple Outputs

In this work, we explore and study the implication of having more than one output on a Genetic Programming (GP) graph-representation. This approach, called, Multiple Interactive Outputs in a Single Tree (MIOST) is based on two ideas: (a) Firstly, we defined an approach, called Interactivity Within an Individual (IWI), which is based on a graph-GP representation. Secondly, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As first step, we analyse the effects of IWI by using only mutations and analyse its implications (i.e., presence of neutrality). Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conduct extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this work, indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.