Abstract-We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is to automatically discover a set of driving rules (if existent) that are general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the current surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favorable generality, in that a single rule can be successfully applied (i) not only for all the section of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics.
This chapter discusses a genetic-algorithm-based approach for selecting a small number of instances from a given data set in a pattern classification problem. Our genetic algorithm also selects a small number of features. The selected instances and features are used as a reference set in a nearest neighbor classifier. Our goal is to improve the classification ability of our nearest neighbor classifier by searching for an appropriate reference set. We first describe the implementation of our genetic algorithm for the instance and feature selection. Next we discuss the definition of a fitness function in our genetic algorithm. Then we examine the classification ability of nearest neighbor classifiers designed by our approach through computer simulations on some data sets. We also examine the effect of the instance and feature selection on the learning of neural networks. It is shown that the instance and feature selection prevents the overfitting of neural networks.
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