  Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful
tools for solving complex optimization problems characterized by multiple,
often conflicting, objectives. While advancements have been made in
computational efficiency as well as diversity and convergence of solutions, a
critical challenge persists: the internal evolutionary mechanisms are opaque to
human users. Drawing upon the successes of explainable AI in explaining complex
algorithms and models, we argue that the need to understand the underlying
evolutionary operators and population dynamics within MOEAs aligns well with a
visual analytics paradigm. This paper introduces ParetoTracker, a visual
analytics framework designed to support the comprehension and inspection of
population dynamics in the evolutionary processes of MOEAs. Informed by
preliminary literature review and expert interviews, the framework establishes
a multi-level analysis scheme, which caters to user engagement and exploration
ranging from examining overall trends in performance metrics to conducting
fine-grained inspections of evolutionary operations. In contrast to
conventional practices that require manual plotting of solutions for each
generation, ParetoTracker facilitates the examination of temporal trends and
dynamics across consecutive generations in an integrated visual interface. The
effectiveness of the framework is demonstrated through case studies and expert
interviews focused on widely adopted benchmark optimization problems.
