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Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for(More)
The past decades have witnessed a rapid growth of Distributed Interactive Multimedia Environments (DIMEs). Despite their intensity of user-involved interaction, the existing evaluation frameworks remain very much system-centric. As a step toward the human-centric paradigm, we present a conceptual framework of Quality of Experience (QoE) in DIMEs, to model,(More)
In this paper we investigate several self-adaptive mechanisms to improve our previous work on NSDE, which is a recent DE variant for numerical optimization. The self-adaptive methods originate from another DE variant, SaDE, but are remarkably modified and extended to fit our NSDE. And thus a self-adaptive NSDE (SaNSDE) is proposed to improve NSDEpsilas(More)
In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G), which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter(More)
—With the increasing adoption of cloud computing for data storage, assuring data service reliability, in terms of data correctness and availability, has been outstanding. While redundancy can be added into the data for reliability, the problem becomes challenging in the " pay-as-you-use " cloud paradigm where we always want to efficiently resolve it for(More)
— In this paper we propose three techniques to improve the performance of one of the major algorithms for large scale continuous global function optimization. Multilevel Cooperative Co-evolution (MLCC) is based on a Cooperative Co-evolutionary framework and employs a technique called random grouping in order to group interacting variables in one(More)
In recent years, Cooperative Coevolution (CC) was proposed as a promising framework for tackling high-dimensional optimization problems. The main idea of CC-based algorithms is to discover which decision variables, i.e, dimensions, of the search space interact. Non-interacting variables can be optimized as separate problems of lower dimensionality.(More)
— In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weighting based cooperative coevolution algorithm, DECC-G [1], which uses random grouping strategy to divide the objective vector into subcomponents, and solve each of them in a cyclical(More)