Models of Representation in Computational Intelligence

Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reasoning, and so on. While CI research generally focuses on the improvement of algorithms (e.g., faster convergence, higher accuracy, reduced error), another promising research direction concerns the representations and models in CI. This can be in the form of search space transformation, that is, dilating, shrinking, stretching, collapsing, or remapping the fitness landscape, leading to the simplified formulations of optimization problems. The use of surrogate modeling can further reduce the complexity - or the computational effort - of a CI task, by providing the optimization algorithm with a simplified or approximated version of the fitness landscape. Moreover, in discrete domains, the simplification of the problem can be obtained by embedding implicit or explicit assumptions into the structure of candidate solutions, so that the feasible search space can be explored by genetic operators in a "smarter" way, reducing the overall computational effort. In the contexts of machine learning or fuzzy modeling, the focus can be on interpretability and explainability, the two open issues that currently affect the trust in AI solutions and hamper the adoption of such techniques in some disciplines (in particular, biomedical applications).