136,292 research outputs found
Distribution functions of linear combinations of lattice polynomials from the uniform distribution
We give the distribution functions, the expected values, and the moments of
linear combinations of lattice polynomials from the uniform distribution.
Linear combinations of lattice polynomials, which include weighted sums, linear
combinations of order statistics, and lattice polynomials, are actually those
continuous functions that reduce to linear functions on each simplex of the
standard triangulation of the unit cube. They are mainly used in aggregation
theory, combinatorial optimization, and game theory, where they are known as
discrete Choquet integrals and Lovasz extensions.Comment: 11 page
(R1886) Effect of Aggregation Function in MOMA-Plus Method For Obtaining Pareto Optimal Solutions
In this work, we have proposed some variants of MOMA-Plus method that we have numerically tested for the resolution of nonlinear multiobjective optimization problems. This MOMA-Plus method and variants differ from each other by the choice of aggregation functions in order to reduce the number of objective functions. The theoretical results allowing us to use these aggregation functions to transform multiobjective optimization problems into single objective optimization problems are proved by two theorems. This study has highlighted the advantages of each aggregation function according to the type of Pareto front of the optimization problem. Six benchmarks test problems have been solved in this work by each of these methods and a comparative study was carried out through the performance indicators which are the differentiation with Pareto front, the convergence to the Pareto front and distributivity on the Pareto front. This allowed us to classify these methods on these benchmarks by using the Graphical Analysis for Interactive Assistance (GAIA) method
Dynamic Objectives Aggregation in Multi-objective Evolutionary Optimization
Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This paper proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature.Multi-objective optimization, Evolutionary algorithms, Aggregate objective functions
COCO: Performance Assessment
We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target values. We argue that runtime is the only available measure
with a generic, meaningful, and quantitative interpretation. We discuss the
choice of the target values, runlength-based targets, and the aggregation of
results by using simulated restarts, averages, and empirical distribution
functions
Supervised learning using a symmetric bilinear form for record linkage
Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk. In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions
On the sensitivity of aggregative multiobjective optimization methods
In this paper, we present a study on the sensitivity of aggregation methods with respect to the weights associated with multiobjective functions of a multiobjective optimization problem. To do this study, we have developed some measurements, such as the speed metric or the distribution metric. We have performed this study on a set of biobjective optimization test problems : a convex, a non convex, a continuous and a combinatorial test problem.
We show that some aggregation methods are more sensitive than others
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