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Abstract

The present invention is a non-linear genetic algorithm for problem solving. The iterative process of the present invention operates on a population of problem solving entities. First, the activated entities perform producing results. Then the results are assigned values and associated with the producing entity. Next, entities having relatively high associated values are selected. The selected entities perform either crossover or fitness proportionate reproduction. In addition other operations such as mutation, permutation, define building blocks and editing may be used. Lastly, the newly created entities are added to the population. This invention disclosed herein is useful for solving at least three groups of problems. The first group of problems consists of a problem that presents itself under several different names, namely, the problem of symbolic function identification, symbolic regression, empirical discovery, modeling, induction, chaos, and forecasting. The second group of problems contains several similar, but different, problems. This group contains the problems of symbolic integration, symbolic differentiation, symbolic solution of differential equations, symbolic solution of integral equations, symbolic solution of mathematical equations, and inverses. The third group of problems contains several other seemingly different, but related, problems, namely, function learning, planning, automatic programming, game playing, concept formulation, pattern recognition, and neural net design. All of these problems can be formulated and then solved in the manner described herein.

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First Claim