Hirotaka Takano

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Structured evolutionary algorithms have been investigated for some time. However, they have been under explored especially in the field of multi-objective optimization. Despite good results, the use of complex dynamics and structures keep the understanding and adoption rate of structured evolutionary algorithms low. Here, we propose a general subpopulation(More)
Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this(More)
Learning classifier systems have been solving reinforcement learning problems for some time. However, they face difficulties under multi-step continuous problems. Adaptation may also become harder with time since the convergence of the population decreases its diversity. This article demonstrate that the novel Self Organizing Classifiers method can cope(More)
Ensembles of classifiers have been studied for some time. It is widely known that weak learners should be accurate and diverse. However, in the real world there are many constraints and few have been said about the robustness of ensembles and how to develop it. In the context of random subspace methods, this paper addresses the question of developing(More)
Multi-stage decision making (MSDM) problems often include changes in practical situations. For example, in the shortest route selection problems in road networks, travelling times of road sections vary depending on traffic conditions. The changes give rise to risks in adopting particular solutions to MSDM problems. Therefore, a method is proposed in this(More)
It is widely known that reinforcement learning is a more general problem than supervised learning. In fact, supervised learning can be seen as a class of reinforcement learning problems. However, only a couple of papers tested reinforcement learning algorithms in supervised learning problems. Here we propose a new and simpler way to abstract supervised(More)
Problems can be categorized as fractured or unfractured ones. A different set of characteristics are needed for learning algorithms to solve each of these two types of problems. However, the exact characteristics needed to solve each type are unclear. This article shows that the division of the input space is one of these characteristics. In other words, a(More)