Abstract. This paper formalizes a family of prioritized multicriteria optimization problems and assesses the corresponding up-to-date known suboptimal solutions. The resulting framework is then employed to characterize and search for Boolean functions which are valuable for a robust symmetric (mainly block) cipher design. The proposed optimality definitions generalize the lexicographic method by establishing an ordered sequence of multiobjective combinatorial optimization problems, which, in turn, gathers the relative relevance of the criteria, so that the optimal solutions can be obtained from a sequential application of the Pareto efficiency. The relationship among the different formulable problems is characterized in terms of both their respective solutions sets and computing costs. Since, in practice, only a limited set of functions can be evaluated (i.e., are known), the best known Pareto efficient functions are also defined. Finally, this framework is employed to obtain new functions having known (Pareto) maximal robustness against linear, differential, randomness-based, interpolation, algebraic and correlation attacks.Key words. Pareto efficiency, combinatorial optimization, block cipher, S-box, balancedness, nonlinearity, algebraic degree, algebraic immunity, absolute indicator, sums-of-squares indicator, correlation immunity order, propagation criterion degree AMS subject classifications. 90C27, 90C29, 68P25, 4904 DOI. 10.1137/16M11078261. Introduction. In recent decades, multicriteria optimization (also known as multiobjective optimization or, in a more general setting, vector optimization) has become an expansive and mature field of research [9,21,28,32]. When operating in such a vast arena, different subfields can be considered depending on the mathematical structures in both the search and criteria sets and the analytical properties of the criteria (or cost) functions. In this framework, multiobjective combinatorial optimization (MOCO) addresses those problems wherein the search set is discrete, usually finite but huge, such that an exhaustive search is not computationally viable.The solution of a multiobjective problem is executed through the following two stages: the proper determination of the optimal solutions set and the multicriteria decision-making among the existing solutions, where preferences among the criteria play an important role. Accordingly, the most established classification of multiobjective techniques relies on the different manners of articulation of such preferences between those two stages: a priori (make decisions before searching), a posteriori (search before making decisions), and progressive (integrating search and decision [21]). In this context, the definition of user preferences among criteria and its application to the efficient design of evolutionary algorithms has been extensively addressed [8,7,23,22,25,26,30,46,53].