IPC분류정보
| 국가/구분 |
United States(US) Patent
등록
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| 국제특허분류(IPC7판) |
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| 출원번호 |
US-0787748
(1991-11-05)
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발명자
/ 주소 |
- Koza John R. (25372 La Rena La. Los Altos Hills CA 94022)
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| 인용정보 |
피인용 횟수 :
213 인용 특허 :
0 |
초록
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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 prod
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.
대표청구항
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In a computer system having a population of programs of various sizes and structures wherein each program is a hierarchical arrangement of functions and arguments, an iterative process for finding a composition of functions whose performance is a good fit, best fit or perfect fit to a sample of data
In a computer system having a population of programs of various sizes and structures wherein each program is a hierarchical arrangement of functions and arguments, an iterative process for finding a composition of functions whose performance is a good fit, best fit or perfect fit to a sample of data, said process comprising iterations of a series of steps, each iteration comprising the steps: executing each said program to produce a result; assigning a value to each said result and associating each said value with a corresponding program which produced each said result, said value indicative of the closeness of the fit of said corresponding program to said sample of data; selecting at least one selected program from said population using selection criteria, said selection criteria based on said value associated with each said program, said selection criteria preferring each said program having a relatively high associated value over each said program having a relatively low associated value; choosing and performing an operation wherein each chosen operation is one of the operations of crossover or reproduction; creating at least one new program by crossover using a group of programs if said chosen operation is crossover, said group of programs comprising said selected program and at least one other program from said population, such that any new program created by crossover comprises at least a portion of said selected program and at least a portion of said other program, said new program can differ in size and shape from said selected program and said other program; retaining said selected program such that said selected program remains unchanged if said chosen operation is reproduction; and adding said new program to said population.
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