Complexity-based speciation and genotype representation for neuroevolution
This work addresses speciation and representation challenges in neuroevolution, offering incremental improvements for researchers in evolutionary algorithms and neural networks.
The paper tackles the problem of speciation and genotype representation in neuroevolution by grouping networks based on hidden neuron count to manage search space complexity, and introduces a novel genotype representation with zero redundancy and high resilience to bloat, demonstrating competitive performance in experiments.
This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space. This speciation principle is indivisibly coupled with a novel genotype representation which is characterised by zero genome redundancy, high resilience to bloat, explicit marking of recurrent connections, as well as an efficient and reproducible stack-based evaluation procedure for networks with arbitrary topology. Furthermore, the proposed speciation principle is employed in several techniques designed to promote and preserve diversity within species and in the ecosystem as a whole. The competitive performance of the proposed framework, named Cortex, is demonstrated through experiments. A highly customisable software platform which implements the concepts proposed in this study is also introduced in the hope that it will serve as a useful and reliable tool for experimentation in the field of neuroevolution.