The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm that evolves high-level controllers for such intelligent vehicles. The algorithm yields a set of solutions that each embody their own prioritisation of various user requirements such as speed, comfort or fuel economy. This contrasts with the current practice in researching such controllers, where user preferences are summarised in a single number that the controller development process then optimises.In this article, we test our multi-objective approach on 6 objectives. Our method outperforms a widely used human behavioural model on many of the objectives. Some performance is lost when we introduce additional objectives, but these losses are small and therefore acceptable. We show that it is possible to evolve a set of vehicle controllers that correspond with different prioritisations of user preferences, giving the driver, on the road, the power to decide which preferences to emphasise, although we do see that the more objectives are added to the system, the less intuitive the prioritisation of the different objectives becomes.
A comparative experiment was carried out with two teaching forms of an introductory laboratory course for freshmen in physics. The results indicate that an individualized version of the course is more effective than a traditional version. The individualized version turns out to be more efficient in learning time but less efficient in teaching time per student. On the average, the students do not differ in their opinion about the attractiveness of the two versions, but the students of the individualized version think that they have spent their time more usefully than the students of the traditional version. There is some indication that the traditional version reinforces an existing positive attitude towards physics experimentation more than the individualized version does. Observations of students doing experiments in the traditional version of the course suggest that these experiments emphasize concrete aspects of experimentation, which may hamper the development of formal skills necessary for good experimental work.
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