This paper describes a method to promote the evolution of the transition rules of cellular automata using a genetic algorithm. We previously proposed the evolutionary design of a cellular automaton in which an applied rule changes with time. This method encodes a rule and the number of times the rule is applied as a chromosome. In this paper, we describe the improvement of the method and analyze rules obtained using the Lambda parameter defined by Langton. The difficulty of test problems in an evolutionary process is adjusted so as to obtain a rule which performs the density classification task with high probability. Experiments using ten-thousand randomly generated tasks have shown that the proposed method performs better than the previous method.

The authors propose a call admission control scheme based on a method of estimating cell loss quality for individual bursty traffic sources. The estimate is expressed in terms of virtual cell loss probability, which may be defined by two traffic characteristic parameters alone: peak and mean rate. The approach is suitable for the estimation of real cell loss probability in heterogeneous and homogeneous traffic models when burst length is larger than buffer capacity. The concept of virtual cell loss probability is extended to the individual call level so as to be able to estimate the quality of service (QOS) provided to individual calls. A virtual bandwidth method is used to develop a practical call admission control system. Quality is ensured by combining a traffic clustering scheme, with a scheme for assigning individual clusters to subcapacities of a link. Priority levels are presented in terms of the class of QOS required, i.e., deterministic or statistical, and the allocation of virtual bandwidth is discussed in terms of both QOS class and traffic characteristics.>