Dear Colleagues,

Evolutionary computation and deep learning are two mainstream technologies of modern artificial intelligence. They are both biology-inspired computational methods but are engaged in different tasks. Usually, evolutionary algorithms are designed to solve complex optimization problems, whereas deep learning models are built to complete complex learning tasks. Recently, many studies have found that the appropriate combination of these two methods provides rich and flexible ways for the two mature paradigms to boost each other.

The purpose of this Special Issue is to gather a collection of the latest studies on the interplay of evolutionary computation and deep learning, from either theoretical or practical perspectives. We welcome new methods that incorporate different deep learning methods to assist evolutionary algorithms in algorithm configuration, evaluation substitution, etc., as well as the methods that apply different evolutionary algorithms to improve deep learning models in terms of the architectures, training procedures, etc. We invite authors to submit research articles and/or review articles that fit this purpose.

Prof. Dr. Yuejiao Gong
Dr. Qiang Yang
Dr. Ting Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• evolutionary computation
• swarm intelligence
• deep learning
• deep reinforcement learning
• graph neural network
• neural architecture search