Sequential Parameter Optimization for Mixed-Discrete Problems

Mixed-discrete optimization deals with mathematical optimization problems with multiple types of variables: discrete (nominal) taking values from a not-sortable set of possible elements, integer variables and variables taking values in a continuous domain. Mixed-discrete problems appear naturally in many contexts such as in the real world in the engineering domain, bioinformatics and data sciences, and this has led to an increased interest in the design of strong algorithms for different variants of the problem. Much effort has been spent over the last decades in studying and developing new methodologies, but unfortunately mixed-discrete optimization problems are much less understood then their “non-mixed” counterparts. In this chapter we will focus on the rather new approaches to handle mixed-discrete problems by means of surrogate methods.