This research is focused on the creation of evolutionary optimization techniques for the solution
of time-changing multi-objective problems. Many optimization problems, ranging from the
design of controllers for time-variant systems to the optimal asset allocation in financial
portfolios, need to satisfy multiple conflicting objectives that change in time. Since most practical
problems involve costly numerical simulations, the goal was to create algorithmic architectures
that increase computational efficiency while being robust and widely applicable.

A combination of two elements lies at the core of the proposed algorithm. First, there is an
anticipatory population that helps the algorithm discover the new optimum when the objective
landscape moves in time. Second, a preservation of the balance between convergence and
diversity in the population which provides an exploration ability to the algorithm. If there is an
amount of predictability in the landscape's temporal change pattern the anticipatory population
increases performance by discovering each timestep's optimal solution using fewer function
evaluations. It does so by estimating the optimal solution's motion with a forecasting model and
then placing anticipatory individuals at the estimated future location. In parallel, the preservation
of diversity ensures that the optimum will be discovered even if the objective's motion is
unpredictable. Together these two elements aim to create a well-performing and robust
algorithmic architecture. Experiments show that the overall concept functions well and that the
anticipatory population increases algorithm performance by up to 30%.

Constraint handling methods for evolutionary algorithms are also implemented, since most of the
problems treated in this work are constrained. In its final form the constraint handling method
applied is a hybrid variant of the Superiority of Feasible Points, which works in a staged manner.

Three different real-world applications are explored. Initially a radar telescope array is optimized
for cost and performance as a practical example of a static multi-objective constrained problem.
Subsequently, two time-changing problems are studied: the design of an industrial controller and
the optimal asset allocation for a financial portfolio. These problems serve as examples of
applications for time-changing multi-objective evolutionary algorithms and inspire the
improvement of the methods proposed in this work.