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—For real-world problems, the task of decision-makers is to identify a solution that can satisfy a set of performance criteria, which are often in conflict with each other. Multi-objective evolutionary algorithms tend to focus on obtaining a family of solutions that represent the trade-offs between the criteria; however ultimately a single solution must be(More)
Preference-inspired co-evolutionary algorithms (PICEAs) are a new class of approaches which have been demonstrated to perform well on multi-objective problems (MOPs). The good performance of PICEAs is largely due to its clever fitness calculation method which is in a competitive co-evolutionary way. However, this fitness calculation method has a potential(More)
Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights \textit{a priori} is difficult. This study proposes a novel algorithm: preference-inspired co-evolutionary algorithm using weights (PICEA-w), which co-evolves a set of weights with the(More)
Preference-inspired co-evolutionary algorithm (PICEA) is a novel class of multi-objective evolutionary algorithm. In PICEA, the usual candidate solutions are guided toward the Pareto optimal front by co-evolving a set of decision maker preferences during the search process. PICEA-g is one realization of PICEAs in which goal vectors are taken as preferences.(More)
Members of the X11/Mint family of multidomain adaptor proteins are composed of a divergent N terminus, a conserved PTB domain and a pair of C-terminal PDZ domains. Many proteins can interact with the PDZ tandem of X11 proteins, although the mechanism of such interactions is unclear. Here we show that the highly conserved C-terminal tail of X11alpha folds(More)
Resource scheduling is a key process for clouds such as Infrastructure as a Service cloud. To make the most efficient use of the resources, we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling problems. We investigate the possibility to place the Virtual Machines in a flexible way to improve the(More)