ECiP @GECCOConf 2022

July 11, 2022 (online)

Evolutionary Computation in Practice

In the Evolutionary Computation in Practice (ECiP) track, well-known speakers with outstanding reputation in academia and industry present background and insider information on how to establish reliable cooperation with industrial partners. They actually run companies or are involved in cooperation between academia and industry. 
If you attend, you will learn multiple ways to extend EC practice beyond the approaches found in textbooks. Experts in real-world optimization with decades of experience share their approaches to creating successful projects for real-world clients. Some of what they do is based on sound project management principles, and some is specific to our type of optimization projects. If you are working in academia and are interested in managing industrial projects, you will receive valuable hints for your own research projects.
In 2021, ECiP will be online (virtual) for the first time, which is a great challenge for a track that relies on personal interactions. We will do our best to enable opportunities for establishing contacts among participants.

Program (Overview)

Session. Day: July, 11th, 2022. Time 10.40 – 2:20

Jamal Toutouh Evolutionary Algorithms Supported Decision Making for Sustainable Cities University of Malaga
Giovanni Iacca Soft skills and soft computing: when presenting the solutions of an optimization problem becomes harder than finding themUniversity of Trento


Soft skills and soft computing: when presenting the solutions of an optimization problem becomes harder than finding them


In this talk, I will briefly discuss some of the most challenging real-world applications of numerical optimization methods (including evolutionary and memetic algorithms) that I’ve been working on with my collaborators in the past few years, ranging from computer aided design to workforce management and scheduling. I will focus mainly on the issues that we have faced whenever we presented the results of an optimization process to stakeholders, the (almost unavoidable) fine-tuning that was often necessary to meet all the users’ needs, and the various kinds of reactions that we have observed (ranging from extremely enthusiastic to unexplainably disappointed). I will finally discuss what are, in my opinion, the main challenges that we need to address whenever evolutionary computation is applied in industry, and I will propose some tentative solutions that may facilitate the adoption of these methods in real-world applications.

Short CV

Giovanni Iacca is an Associate Professor in Computer Engineering at the Department of Information Engineering and Computer Science of the University of Trento, Italy, where he founded the Distributed Intelligence and Optimization Lab (DIOL). Previously, he worked as postdoctoral researcher in Germany (RWTH Aachen, 2017-2018), Switzerland (University of Lausanne and EPFL, 2013-2016) and The Netherlands (INCAS3, 2012-2016), as well as in industry in the areas of software engineering and industrial automation. He was co-PI of the FET-Open project “PHOENIX” (2015-2019), and currently is co-PI of the PATHFINDER-CHALLENGE project “SUSTAIN” (2022-2026). He has received two best paper awards (EvoApps 2017 and UKCI 2012). His research focuses on computational intelligence, stochastic optimization, and distributed systems. In these fields, he co-authored more than 100 peer-reviewed publications. He is actively involved in the organization of tracks and workshops at some of the top conferences in the field of computational intelligence, and he regularly serves as reviewer for several journals and conference committees.

Evolutionary Algorithms Supported Decision Making for Sustainable Cities 


In recent years, smart cities have been increasingly concerned with meeting sustainable development goals (SDGs). These goals encompass social, environmental, and economic aspects to make cities inclusive, safe, resilient, and sustainable. Pursuing these objectives requires a complex decision-making process because it requires considering simultaneously multiple, often conflicting, design criteria (such as the economical cost and the quality of service provided). This can be faced by modeling the cities and addressing the decisions as optimization problems realistically considering the constraints, the variables, and the (likely conflicting) objectives. Evolutionary Algorithms (EAs) have shown remarkable success in solving these problems, providing solutions that the decision-makers can consider. This talk aims to present the work carried out by our research group on using EAs to successfully face urban challenges related to waste management, electric vehicles, intelligent transportation systems, public transportation design, etc. 


Jamal Toutouh holds a Tenure Track to Associate Professor at the University of Málaga (Spain). Previously, he was a Marie Skłodowska Curie Postdoctoral Fellow at Massachusetts Institute of Technology (MIT) in the USA, at the MIT CSAIL Lab. During his doctoral studies at the University of Málaga, Jamal analyzed and devised Evolutionary Computation methods to address Smart Mobility, one of the main challenges in modern sustainable cities. His dissertation was awarded several prizes, including the Best Spanish PhD Thesis in Smart Cities (2018). Currently, he is a Generative Machine Learning enthusiast. His research explores the combination of Nature-inspired, gradient-free approaches and gradient-based methods to address Deep Generative Modeling. All this while proposing solutions based on Evolutionary Computation to foster more sustainable societies.