This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper aims on the experimental investigations on the chaos-driven evolutionary algo...
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This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper aims on the experimental investigations on the chaos-driven evolutionary algorithm Differential Evolution (DE) concept. This research represents the continuation of the preliminary satisfactory results obtained by means of chaos embedded (driven) DE, which utilizes the chaotic dynamics in the place of pseudorandom number generators The novelty of this work represents the experimental analysis of the chaotic dynamics directly injected into the DE. To be more precise, this research investigates the influence of parameter settings to the performance of chaos driven DE. Both settings for mutation and crossover DE control parameters and adjustable chaotic system parameters are experimentally investigated here. Repeated simulations were performed on the selected set of well-known benchmark functions in higher dimensions. Finally, the obtained results are compared with canonical DE and state-of-the art representative of basic adaptive variant jDE.
Zebrafish animal is considered as one of the most suitable animals to test toxicity of compounds due many features such as transparency and a large number of embryos produced in each mating. The main problem of the ze...
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Zebrafish animal is considered as one of the most suitable animals to test toxicity of compounds due many features such as transparency and a large number of embryos produced in each mating. The main problem of the zebrafish-based toxicity test is the manual inspection of thousands of animals images in different phases and this is not feasible enough for the analysis, i.e. it is slow and may be inaccurate process. To help addressing this problem, in this paper, an automated classification of alive (healthy) and coagulant (died because of toxic compounds) zebrafish embryos are proposed. The embryos’ images are used to extract some features using the Segmentation-based Fractal Texture Analysis (SFTA) technique. The Rotation Forest classifier is then used to match between testing and training features (i.e. to classify alive and coagulant embryos). The experiments have proved that choosing threshold value of SFTA technique and the size of the rotation forest classifier have a great impact on the classification accuracy. With accuracy around 99.98%, the experimental results have showed that the proposed model is a very promising step toward a fully automated toxicity test during drug discovery.
Software-based methods for the detection of control-flow errors caused by transient fault usually consist in the introduction of protecting instructions both at the beginning and at the end of basic blocks. These meth...
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Software-based methods for the detection of control-flow errors caused by transient fault usually consist in the introduction of protecting instructions both at the beginning and at the end of basic blocks. These methods are conservative in nature, in the sense that they assume that all blocks have the same probability of being the target of control flow errors. Because of that assumption they can lead to a considerable increase both in memory and performance overhead during execution time. In this paper, we propose a static analysis that provide a more refined information about which basic blocks can be the target of control-flow-errors caused by single-bit flips. This information can then be used to guide a program transformation in which only susceptible blocks have to be protected. We implemented the static analysis and program transformation in the context of the LLVM framework and performed an extensive fault injection campaign. Our experiments show that this less conservative approach can potentially lead to gains both in memory usage and in execution time while keeping high fault coverage.
In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two dif...
In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail.
In this paper it is discussed and briefly experimentally investigated the performance of multi-swarm PSO with super-sized swarms. The selection of proper population size is crucial for successful PSO using. This work ...
In this paper it is discussed and briefly experimentally investigated the performance of multi-swarm PSO with super-sized swarms. The selection of proper population size is crucial for successful PSO using. This work follows previous promising research.
In this paper several previously successfully tested approaches for PSO algorithm are merged. The Multiple-Choice strategy for PSO algorithm with chaotic pseudorandom number generator based on chaotic Dissipative Stan...
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ISBN:
(纸本)9781479974931
In this paper several previously successfully tested approaches for PSO algorithm are merged. The Multiple-Choice strategy for PSO algorithm with chaotic pseudorandom number generator based on chaotic Dissipative Standard Map is enhanced with basic Dimensional mutation technique. The particular chaotic map selection was based on previous long-term research of chaos driven PSO. The approach is tested on a set of well-known benchmark functions with variety of dimensional settings and strict limit of cost function evaluations. Thus the potential of this method for needs of fast and real-time optimization is investigated. Promising results presented in the results section and briefly analyzed.
Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and c...
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ISBN:
(纸本)9781479986989
Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and classification methods in place of traditional approaches based on analog technology such as the pulse rise-time and charge-comparison methods. This work compares the ability of evolutionary fuzzy rules and support vector machines to perform accurate neutron-gamma classification. The accuracy and performance of both investigated methods are evaluated on two real-world data sets.
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