Optimisation and Machine Learning tools are among the most used tools in the modern world with their omnipresent computing devices. Yet, while both these tools rely on search processes (search for a solution or a model able to produce solutions), their dynamics have not been fully understood. This scarcity of knowledge on the inner workings of heuristic methods is largely attributed to the complexity of the underlying processes, which cannot be subjected to a complete theoretical analysis. However, this is also partially due to a superficial experimental setup and, therefore, a superficial interpretation of numerical results. Researchers and practitioners typically only look at the final result produced by these methods. Meanwhile, a great deal of information is wasted in the run. In light of such considerations, it is now becoming more evident that such information can be useful and that some design principles should be defined that allow for online or offline analysis of the processes taking place in the population and their dynamics.

Hence, with this workshop, we call for both theoretical and empirical achievements in identifying the desired features of optimisation and machine learning algorithms, quantifying the importance of such features, spotting the presence of intrinsic structural biases and other undesired algorithmic flaws, studying the transitions in algorithmic behaviour in terms of convergence, any-time behaviour, traditional and alternative performance measures, robustness, exploration vs exploitation balance, diversity, algorithmic complexity, etc., to gather the most recent advances to fill the aforementioned knowledge gap and disseminate the current state-of-the-art within the research community.
Thus, we encourage submissions exploiting carefully designed experiments or data-heavy approaches that can come to help in analysing primary algorithmic behaviours and modelling internal dynamics causing them.