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Recently, we have witnessed an infusion of calculating models based on models offered by nature, models with more or less fidelity to the original that have led to the development of various problem-solving computational procedures. Starting from the observation of natural processes at the macroscopic or microscopic level, various methods have been developed. Technological progress today allows the...
The paper presents a new technique for the control of the population's dynamic in evolutionary optimization. The technique is inspired by the endocrine system, respectively, by the control mechanism of the hormones concentration. The approach is suitable for multimodal optimization in static or dynamic environment. The major advantage of the proposal is given by the fact that there is no need of a...
A problem arises in data mining, when classifying unbalanced datasets using Support Vector Machines. Because of the uneven distribution and the soft margin of the classifier, the algorithm tries to improve the general accuracy of classifying a dataset, and in this process it might misclassify a lot of weakly represented classes, confusing their class instances as overshoot values that appear in the...
The behavior of standard evolutionary algorithm in the case of multi-modal optimization problems meets a major difficulty. It generally converges towards a single optimum point failing to maintain in the population the multiple optima of the problem under consideration. Various methods enrich the standard algorithm to obtain efficient techniques for solving multi-modal problems. These methods mainly...
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