| 摘要 |
In a computer implemented learning and/or process control system, a computer model is constituted by the most currently fit entity in a population of computer program entities. The computer model defines fitness as a function of inputs and outputs. A computing unit accesses the model with a set of inputs, and determines a set of outputs for which the fitness is highest. This associates a sensory-motor (input-output) state with a fitness in a manner that might be termed "feeling".The learning and/or control system preferably utilizes a Compiling Genetic Programming System (CGPS) in which one or more machine code entities such as functions are created which represent solutions to a problem and are directly executable by a computer. The programs are created and altered by a program in a higher level language such as "C" which is not directly executable, but requires translation into executable machine code through compilation, interpretation, translation, etc. The entities are initially created as an integer array that can be altered by the program as data, and are executed by the program by recasting a pointer to the array as a function type. The entities are evaluated by executing them with training data as inputs, and calculating fitnesses based on a predetermined criterion. The entities are then altered based on their fitnesses using a genetic machine learning algorithm by recasting the pointer to the array as a data (e.g. integer) type. This process is iteratively repeated until an end criterion is reached.
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