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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)
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 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)
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)
The neural mechanisms used by the human brain to identify phonemes remain unclear. We recorded the EEG signals evoked by repeated presentation of 12 American English phonemes. A support vector machine model correctly recognized a high percentage of the EEG brain wave recordings represented by their phases, which were expressed in discrete Fourier transform(More)
The existing localization technology with single mode is limited in accuracy and robustness. To obtain higher accuracy, this paper proposes a novel indoor localization algorithm with WI-FI and Bluetooth. The approach is based on the Bayesian filtering and performs data-level fusion to get the final position estimate. In addition, idea of simulated annealing(More)