Monte Carlo Tree Search approach with Pareto optimality and pocket algorithm is used to solve and optimize the multi-objective constraint-based staff scheduling problem. The proposed approach has a two-stage selection strategy and the experimental results show that the approach is able to produce solutions for cooperative games.

A non-cooperative finite game of several persons is considered in the case, where payoff functions are linear. Extreme levels of independent perturbations of payoff functions parameters, which remain Nash and Pareto optimality of a situation, are specified. Necessary and sufficient conditions of such stability are stated. AMS Mathematics Subject Classification : 90C27, 90C29.

We present new necessary optimality conditions for εe–Pareto optimal solutions of constrained vector optimization problems with empty interior ordering cones. We use the dual-space approach based on advanced tools of variational analysis and generalized differentiation. It allows us not implement any scalarization technique while be able to establish necessary results for nonconvex and nonsolid...

Patterns are the key building blocks in the logical analysis of data (LAD). It has been observed in empirical studies and practical applications that some patterns are more “suitable” than others for use in LAD. In this paper, we model various such suitability criteria as partial preorders defined on the set of patterns. We introduce three such preferences, and describe patterns which are Paret...

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. In this paper, we design a genetic algorithm for this purpose. We compose multiple...

We propose a test generation method employing genetic evolutionary algorithms, and multi-objective optimising strategies that achieve Pareto optimality. The generated tests enable high coverage for the design under test and use low computational resources. The benefits of the technique are demonstrated by its application for system-on-chip verification.

A characterization theorem of the core of two-sided matching problems is proved. The theorem characterizes the core by means of four axioms, the Pareto optimality, consistency, converse consistency, and anonymity. It is shown that all of them are logically independent.

This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distributions satisfying a monotone likelihood ratio property. Included in this class of distributions are the Student’s t, the Pareto, and many other distributions. We prove some general asymptotic optimality results under fixed and random thresholding. As examples of these general results, we establish the Bay...