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Non-linear genetic algorithms for solving problems by finding a fit composition of functions

  • US 5,136,686 A
  • Filed: 11/05/1991
  • Issued: 08/04/1992
  • Est. Priority Date: 03/28/1990
  • Status: Expired due to Term
First Claim
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1. 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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