A search method using an evolutionary algorithm such as a genetic algorithm (GA) is very effective if the parameter is appropriately set. However, the optimum parameter setting was so difficult that each optimal method depending on each problem pattern must be developed one by one. Therefore, this has required special expertise and large amounts of verification experiment. In order to solve this problem, a new method called "adaptive parameter control" is proposed, which adaptively controls parameters of an evolutionary algorithm. However, since this method just increases the selection probability of a search operator that generated a well evaluated individual, this is apt to be a shortsighted optimization method. On the contrary, a method is proposed to realize longsighted optimal parameter control of GA using reinforcement learning. However, this method does neither consider the calculation cost of search operators nor multipoint search characteristics of GA. This paper proposes a method to efficiently control parameters of an evolutionary algorithm by using the reinforcement learning where the reward decision rules are elaborately incorporated under the consideration of GA's multipoint search characteristics and calculation cost of the search operator. It is expected that this method can efficiently learn parameters to optimally select search operators of GA for approximately solving Travelling Salesman Problems (TSPs).
A case of scleredema of Buschke associated with rheumatoid arthritis and Sjögren's syndrome is described. The onset of the skin changes and rheumatoid arthritis was almost simultaneous and the sicca syndrome developed 4 years later.
Abstract-AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans' desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling -at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners' paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives.
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