On completion of the course, the student has achieved the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge The candidate...

has an in-depth understanding of the main characteristics defining search and optimization problems, and how specific software engineering problems can be reformulated as optimization problems, for example how test case generation can be formulated as an optimization problem.
has knowledge of the main characteristics of the most used state of the art search algorithms, their benefits and drawbacks, and their applicability to specific types of optimization problems. For example, understanding the critical difference between exploration and exploitation of the search landscape, and the impact of this difference on the solutions being generated for specific optimization problems.
can critically assess and discuss the details of how evolution-inspired algorithms work, as well as their suitability for individual problems and applications.
has an in-depth understanding of how search-based algorithms can be customized for individual problems and can critically discuss the suitability of particular operators on both familiar and newly defined optimization problems. For example, the impact that fitness functions and specialized search operators have on the search outcomes.
has an in-depth understanding of how to analyze and compare the results of experiments with search algorithms.

Skills The candidate can...

define problems they encounter as optimization problems, design relevant fitness functions and search operators that can exploit domain knowledge of those problems, and critically assess the benefits and drawbacks of those operators on the respective problems
assess the suitability of existing evolutionary algorithms and, if needed, implement their own customized evolutionary algorithms to better exploit domain knowledge of optimization problems.
individually use and extend existing open-source research tools used in the field of Search-Based Software Engineering, identifying and working to address particular gaps in the current body of knowledge.
run experiments and prepare scripts to automatically analyze the results with statistical tests, automatically generating Latex tables and graphs to visualize the data results.

General competence The candidate can...

discuss, propose, and critically evaluate search and optimization problem formulations, in both their work and other research.
apply and assess evolutionary algorithms, their suitability to individual research problems, the quality of their implementation, and the implications of their results,
can conduct relevant research within the field of Search-Based Software Engineering.

Subject description PIT9206

Evolutionary Computation and Search-Based Software Engineering