Domenico Quagliarella

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In this work a parallel multi-objective genetic algorithm is presented. The population selection and mating phase is kept distinct from the population fitness evaluation loop, that is implemented in parallel. The population can be logically split in sub-populations which number does not depend on the number of processors available for computation. Different(More)
An optimization procedure aimed at the design of multicomponent airfoils for high-lift applications is described. The procedure is based on a multiobjective genetic algorithm; two #ow solvers have been coupled with the genetic algorithm: a viscous}inviscid interaction method, based on an Euler #ow solver and an integral boundary layer routine, and a method(More)
The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their application to multi-criteria design problems. A short introduction to multi-point aerodynamic shape design is given, and the advantages of a multi-objective optimization approach to this problem are outlined. The introduction of basic concepts of(More)
This paper proposes a five different Genetic Algorithms for the Resource Constrained Project Scheduling Problem (RCPSP) and a comparison between them is proposed. This work employs Genetics Algorithms (GA) to schedule project activities to minimize the makespan subject to precedence constraints and resources availability. The GAs were programmed using Java(More)
A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define the objectives of the optimization process, the inverse(More)
Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization(More)
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