Naoki Hamada

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This paper presents a new Pareto frontier covering strategy for the functional-specialization multi-objective genetic algorithm (FS-MOGA). FS-MOGA is a real-coded GA for multi-objective function optimization proposed by Hamada et. al. FS-MOGA utilizes the local-Pareto-optima overcoming strategy and the Pareto frontier covering strategy adaptively. Hamada(More)
The recent development of multi-agent simulations brings about a need for population synthesis. It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin. This paper presents a new kernel density estimator for this task. Our method is an analogue of(More)
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