Following the success of the previous edition at GECCO2017 “Workshop on Evolutionary Algorithms for Smart Grids (SmartEA)”, the main goal of this workshop is to promote the research on evolutionary algorithms in smart grids. We are seeking innovative research articles including, but not limited to the following areas:

- Energy generation and load forecasting
- Demand side and smart home energy management
- Network Restoration
- Real-time control and optimization
- Smart micro-grids
- Applications of Evolutionary computation in the context of smart grids

Submitted work should put an emphasis on modeling of solution spaces, on finding optimal representations and operators for evolutionary algorithms, and on employing and developing advanced evolutionary heuristics, e.g., for step size control, constraint handling, dynamic solution spaces, and multiple conflictive objectives.

Submission guidelines
Workshop papers must be submitted using the GECCO submission site. Detailed instructions can be found at https://gecco-2019.sigevo.org/index.html/Workshops

Submitted papers must not exceed 8 pages (excluding references) and are required to be in compliance with the GECCO 2019 Papers Submission Instructions. It is recommended to use the same templates as the papers submitted to the main tracks. It is not required to remove the author information.

All accepted papers will be presented at the corresponding workshop and appear in the GECCO Conference Companion Proceedings.

Important dates
These dates are strict, no extensions will be granted.
• Submission opening: February 27, 2019
• Submission deadline: April 3, 2019
• Notification of acceptance: April 17, 2019
• Camera-Ready Material: April 24, 2019
• Author registration deadline: April 24, 2019


Organizers

Fernando Lezama, GECAD, Polytechnic of Porto
(flzcl@isep.ipp.pt)
Joao Soares, GECAD, Polytechnic of Porto
(joaps@isep.ipp.pt)
Zita Vale, GECAD, Polytechnic of Porto
(zav@isep.ipp.pt)


This Special Session is under activities of IEEE CIS Task Force 3 on Computational Intelligence in the Energy Domain