Quantum cascade laser (QCL) is a specific type of semiconductor laser that operates through principles of quantum mechanics. Because there is a genuine lack of compact and coherent devices which can operate in the far...
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Sequential learnable evolutionary algorithm (SLEA) provides an algorithm selection framework for solving the black box continuous design optimization problems. An algorithm pool consists of set of established algorith...
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Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA s...
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This paper presents a heuristic for directing the neighbourhood (mutation operator) of stochastic optimisers, such as evolutionary algorithms, so to improve performance for the flowshop sequencing problem. Based on id...
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evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing de...
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This paper deals with broadcasting warning / emergency messages in mobile ad hoc networks. Traditional broadcasting schemes tend to focus on usually high and homogeneous neighborhood densities environments. This paper...
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The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in...
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Multiple data sources must be taken into account in several application areas. Each of those data views often offers a unique viewpoint on a certain group of things. Several data perspectives with varying degrees of d...
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This paper describes a new data mining algorithm to learn Bayesian networks structures based on an extending evolutionary programming (EP) method and the Minimum Description Length (MDL) principle. Aiming at preventin...
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ISBN:
(纸本)0780384032
This paper describes a new data mining algorithm to learn Bayesian networks structures based on an extending evolutionary programming (EP) method and the Minimum Description Length (MDL) principle. Aiming at preventing and overcoming premature convergence, the algorithm combines the niche technology into the selection mechanism of EP. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. To evaluate the performance of our algorithm, we conduct a series of experiments and compare them with previous work based on genetic algorithms (GA). The experimental results illustrate that both quality of the solutions and computational time of our algorithm are superior.
To successfully search multiple coadaptive subcomponents in a solution, we developed a novel cooperative evolutionary algorithm based on a new computational multilevel selection framework. This algorithm constructs co...
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ISBN:
(纸本)9781450300728
To successfully search multiple coadaptive subcomponents in a solution, we developed a novel cooperative evolutionary algorithm based on a new computational multilevel selection framework. This algorithm constructs cooperative solutions hierarchically by implementing the idea of group selection. We show that this simple and straightforward algorithm is able to accelerate evolutionary speed and improve solution accuracy on string covering problems as compared to other EAs used in literature. In addition, the structure of the solution and the roles played by each subcomponent in the solution emerge as a result of evolution without human interference. Copyright 2010 ACM.
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