Carolina P. de Almeida

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Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Upper Confidence Bound (UCB) algorithms have been successfully applied for this task due to its ability to tackle the Exploration versus Exploitation (EvE) dilemma presented on AOS.(More)
This paper presents Intelligent Systems based on Natural Computing for solving economic load dispatch problems. The proposed methods use three different natural computing techniques: Cultural Algorithms, Artificial Immune Systems and Fuzzy Inference Systems. The base for the methods is a realcoded Immune System centred on the clonal selection principle. Two(More)
Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first time a(More)