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The purpose of this paper is to evaluate the performance of a new multiobjective algorithm called Vector Evaluated Population Based Incremental Learning (VEPBIL). The new algorithm was applied in solving a real world application named Reinsurance Contract Optimization (RCO), which is a multiobjective problem consisting of maximizing two conflicting functions: expected return and risk. The VEPBIL was...
The purpose of this paper is to evaluate the performance of Vector Evaluated Differential Evolution (VEDE) and Vector Evaluated Particle Swarm Optimization (VEPSO) in solving a real world financial optimization problem. The algorithms have been applied to the Reinsurance Contract Problem, which is a challenging problem in computational finance, and their performance has been evaluated in terms of...
In this paper four different algorithms are proposed to solve the QoS-aware Web Services Composition (QWSC) problem in six different search-space sizes and a realistic deadline (a point not covered in many related works). Differently from some related works, statistical techniques are adopted in this paper in order to ensure more precise results from the algorithms. Two new algorithms are proposed:...
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