Javier Poyatos

TL;DR: In this paper, the authors present a taxonomy of nature-inspired and bio-inspired algorithms, and provide a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.

TL;DR: In this article, the authors review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account, and expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.

TL;DR: Two comprehensive, principle-based taxonomies are proposed that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm.

TL;DR: This work comprehensively review and critically examine contributions made so far based on three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

TL;DR: The authors comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in deep learning, and a taxonomy associated with an in-depth analysis of the literature, which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of literature, and new directions of research (What can be done, and what for?).