A novel technique to self-adapt parameters in parallel/distributed genetic programming

This paper introduces the Supervisor Evolutionary Algorithm, a novel technique that allows for self-adapt almost all the internal parameters in parallel distributed client-server genetic programming. This novel adapting mechanism, is itself of an evolutionary nature, so we have a double evolutionary tool. The upper level, as is usual in evolutionary computing, has its own customized selection, crossover, and mutation mechanisms. The lower stage used here is the Brain Project a parallel-distributed software tool for formal modelling of numerical data using a hybrid neural-genetic programming technique. As demonstrated by the experiment reported in this paper, our approach works well adapting continuously its internal parameters.