Abstract: A new probabilistic computing system (PCS) provides computational functionality needed to efficiently realize randomized computing methods in otherwise standard, deterministic digital computing systems. The PCS may be incorporated in a standard computing platform such as a PC or workstation. In the PCS, a computational path includes a random access memory (RAM) where a predetermined computing problem is stored in conjunctive normal form. A nondeterministic subsystem generates random binary values forming a proposed solution to the problem, which solution is rapidly checked through a crosspoint switch array coupled to the RAM. The computational path essentially runs asynchronously, while a delay circuit provides delay and timing signals for interfacing with external DRAM, as well as a synchronizing signal for operation of several of the PCS systems together for enhanced performance.

Abstract: The present invention is a method of evolving classifier programs for signal processing and control. The present invention uses an ‘evolver’ program examine a large number of potential features, which may be from multiple signals to create a ‘classifier’ program. The output of the classifier program is compared to the desired output. One or more classifier programs is then created and optimized by the evolver program by means of genetic programming. The desired output is again compared to actual classifier program output and the difference is used as a measure of fitness to guide the evolution of the classifier program. The optimized classifier program produced by the present invention is fitter than the background art at correctly providing a repeatable and accurate output, especially for complex and simultaneous input signals.