Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model
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Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classif ...
In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort ...
Institute of Electrical and Electronics Engineers2011
1 Introduction to energy systems and evolutionary algorithms. 2 Genetic Algorithm for Constrained Optimization Models. 3 A Framework for Multi-Objective Evolutionary Algorithms. 4 Thermo-Economic Optimisation of a Solar Thermal Plant. ...
Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficie ...
This work presents a synthesis method that leads to the preliminary design of industrial energy systems. Such systems are composed of several technologies that transform, through a set of physical unit operations, raw materials and energy into products and ...
This paper presents a new double hybridized genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The first hybridization adopts embryonic chromosomes as prefixes of variable orders instead of complete variable or ...
The paper presents an optimization design of an in-wheel BLDC motor for a kick scooter. The optimization is performed using a genetic optimization tool combined with a FEM commercial software. The new contributions of the paper are: (i) introduction of thr ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solutio ...
Most bioimaging modalities rely on indirect measurements of the quantity under investigation. The image is obtained as the result of an optimization problem involving a physical model of the measurement system. Due to the ill-posedness of the above proble ...