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This paper presents an optimization methodology of hybrid multi-objective genetic algorithm for the real world optimization problem of facial analysis of multiple camera images by 2.5D Active Appearance Model (AAM). Facial large lateral movements make acquisition and analysis of facial images by single camera inefficient. Moreover non-convex multidimensional facial search space formed by AAM requires...
Chromosome representation to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for optimal design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better...
The paper presents a method of multi-objective piece-wise evolutionary registration of medical images. The evolutionary search is transferred from the actual image into response space, which accounts for the reliable extraction of the main shape features while avoiding the direct comparison of the pixel values. A composite two-layer image model is constructed whereby the first layer outlines the principal...
A hybrid genetic algorithm is proposed to deal with the shape optimization of the truss based on relative difference quotient method and improved genetic algorithm. The advantages of the genetic algorithm in global optimization and the relative difference quotient method in local searching ability are both included in the HGA method. Numerical example of a 37-bar truss was given to demonstrate the...
System of systems (SoS) architecting techniques rely on traditional, static tools that were designed for classical stove-piped systems. There is a need for tools that can capture the complex adaptive nature of such SoS. An architecture search methodology using genetic algorithms and a fuzzy assessor was applied to the conceptual architecture design of a generic smart grid and a set of architectures...
We propose a self-adaptive hybrid evolutionary algorithm for the optimization of Morse clusters. The approach relies on a two-phase local optimization method to efficiently guide search. Individuals encode its own penalty settings and the algorithm evolves them simultaneously with the search for low energy clusters. Results show that the approach is efficient, as it is able to discover all optimal...
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome...
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