Evolutionary machine-learning (EML) could be easily defined as a crossbreed between the fields of evolutionary computation (EC) and machine learning (ML). However, as the “obvious connection” between the processes of learning and evolution has been pointed out by Turing back in 1950, to avoid a blatant pleonasm, the term is mostly used referring to the integration of well-established EC techniques and canonical ML frameworks.

A first line of research ascribable to EML predates the recent ML bonanza and focuses on using EC algorithms to optimize frameworks: it included remarkable studies in the 1990s, such as the attempts to determine optimal topologies for an artificial neural network using a genetic algorithm. The other way around, a line tackling the use of ML techniques to boost EC algorithms appeared before 2000. More recently, scholars are proposing truly hybrid approaches, where EC algorithms are deeply embedded in frameworks performing ML tasks.

The workshop’s topics of interest include but are not limited to:

• EA applied to ML tasks (e.g., evolutionary classifiers)
• EA applied to ML algorithms (e.g., evolutionary optimization of deep neural network)
• ML applied to EA algorithms (e.g., optimal parameter prediction)
• Real world applications of EML

The workshop will be held during the conference Parallel Problem Solving From Nature (PPSN) 2018.

Submission

• Format and page length: 2-4 page abstract PDF file in Springer LNCS format
• Updated Deadline: July 1, 2018
• How to submit: by email to the workshop organizers ([log in to unmask] and [log in to unmask])

Details

• Workshop date: 8 or 9 September 2018
• Location: Coimbra, Portugal