A Study of Fitness Landscapes for Neuroevolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Rodrigues:2020:CEC,

• author = "Nuno M. Rodrigues and Sara Silva and Leonardo Vanneschi",
• booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
• title = "A Study of Fitness Landscapes for Neuroevolution",
• year = "2020",
• editor = "Yaochu Jin",
• month = "19-24 " # jul,
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-7281-6929-3",
• DOI = "doi:10.1109/CEC48606.2020.9185783",
• abstract = "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.",
• notes = "Also known as \cite{9185783}",

}

Genetic Programming entries for Nuno Miguel Rodrigues Domingos Sara Silva Leonardo Vanneschi

Citations