time is discrete as opposed to continuous. Proc. Natl. Acad. Sciences (USA). In press. Over the past decade, the increasing speed and availability of powerful computers has made computer simulations an attractive method to research the behaviours of complex systems. In the physical sciences, simulation

Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main

Parallel discrete event simulation (PDES), sometimes I called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises

of designing signal processing and communications systems, ranging from algorithms and communication strategies, simulation, hardware and software design, parallel computing, and generating real-time prototypes. To accommodate this breadth, Ptolemy must support a plethora of widely-differing design styles

A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in com-putation time of at most a polynomial factol: It is not clear whether this is still true when quantum mechanics is taken

This paper provides tables of critical values for some popular tests of cointegration and unit roots. Although these tables are necessarily based on computer simulations, they are much more accurate than those previously available. The results of the simulation experiments are summarized by means

DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools

A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration

or differential equations, which are verified through computer simulations. The analysis provides convenient approximate or exact solutions as well as useful convergence time and growth ratio estimates. The paper recommends practical application of the analyses and suggests a number of paths for more detailed

network like model. The validity of the new learning algorithm are verified by computer simulations.