A Study of Fitness Landscapes for Neuroevolution
This work addresses a gap in analyzing neuroevolution performance for researchers in evolutionary computation and machine learning, but it is incremental as it extends existing fitness landscape methods to a new domain.
The paper tackled the problem of applying fitness landscapes to neuroevolution for the first time, using them to study optimization power and generalization ability, and found that autocorrelation and entropic ruggedness measures are appropriate for estimating these aspects.
Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution. This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method. More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them. To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness. The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.