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Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian...
Network-based Semi-Supervised Learning (NbSSL) propagates labels in affinity-networks by taking advantage of the network topology likewise information spreading in trust networks. In NbSSL, not only the unlabeled data instances, but also the labeled ones, are able to bias the classification performance. Herein, we show some results and discussion on this phenomenon. Even the suitability of the free...
It has been shown recently that unconstrained particles that follow the position and velocity update rules of a standard global best particle swarm optimization algorithm leave the boundaries of the search space within the first few iterations of the search process. Provided that a better solution does not exist outside of the search boundaries, these roaming particles are eventually pulled back within...
Bin packing is a classical combinatorial optimization problem which has a wide range of real-world applications in industry, logistics, transport, parallel computing, circuit design and other domains. While usually presented as discrete problems, we consider here continuous packing problems including curve shapes, and model these problems as continuous optimization problems with a multi-objective...
The amount of data generated in different knowledge areas has made necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to...
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