Adaptive optimization methods
First Claim
1. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
- generating a solution set;
evaluating solutions in said solution set in the one or more computers;
selecting desirable solutions from the solution set and saving said selected desirable solutions in the one or more computers;
creating a structural model in the one or more computers using said desirable solutions;
creating a surrogate fitness model in the one or more computers based on said structural model and said desirable solutions;
generating a new solution set;
wherein said generating a new solution set comprises;
analyzing at least one of said structural model and said surrogate fitness model;
determining a method for generating a new solution set based at least in part on said analyzing;
generating a new solution set based on said determined method.
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Abstract
Methods and systems for optimizing a solution set. A solution set is generated, and solutions in the solution set are evaluated. Desirable solutions from the solution set are selected. A structural model is created using the desirable solutions, and a surrogate fitness model is created based on the structural model and the desirable solutions. A new solution set may be generated and/or evaluated, based on analyzing at least one of the structural model and the surrogate fitness model, and determining a method for generating a new solution set and/or evaluating the new solution set based at least in part on the analyzing.
55 Citations
23 Claims
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1. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
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generating a solution set; evaluating solutions in said solution set in the one or more computers; selecting desirable solutions from the solution set and saving said selected desirable solutions in the one or more computers; creating a structural model in the one or more computers using said desirable solutions; creating a surrogate fitness model in the one or more computers based on said structural model and said desirable solutions; generating a new solution set; wherein said generating a new solution set comprises; analyzing at least one of said structural model and said surrogate fitness model; determining a method for generating a new solution set based at least in part on said analyzing; generating a new solution set based on said determined method. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
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generating a solution set; evaluating solutions in said solution set in the one or more computers; selecting desirable solutions from the solution set and saving said selected desirable solutions in the one or more computers; creating a structural model in the one or more computers using said desirable solutions; creating a surrogate fitness model in the one or more computers based on said structural model and said desirable solutions; generating a new solution set at least partly based on said created structural model; determining if completion criteria are satisfied; if completion criteria are not satisfied, evaluating solutions in said new solution set in the one or more computers; wherein said evaluating solutions in said new solution set comprises; analyzing at least one of said structural model and said surrogate fitness model in the one or more computers; determining a method for evaluating solutions based at least in part on said analyzing; evaluating solutions in said new solution set in the one or more computers based on said determined method; wherein said analyzing comprises analyzing said structural model to infer a topology of a parallel function evaluation, and wherein said determining a method comprises determining an evaluation method using said inferred topology. - View Dependent Claims (14)
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15. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
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generating a solution set; evaluating solutions in said solution set in the one or more computers; selecting desirable solutions from the solution set and saving said desirable solutions in the one or more computers; creating a structural model in the one or more computers using said desirable solutions; creating a surrogate fitness model in the one or more computers based on said structural model and said desirable solutions; analyzing at least one of said structural model and said surrogate fitness model in the one or more computers; generating a new solution set in the one or more computers; determining if completion criteria are satisfied; if completion criteria are not satisfied, evaluating solutions in said new solution set; wherein said generating a new solution set comprises; determining a method for generating a new solution set based at least in part on said analyzing; generating a new solution set based on said determined method; wherein said evaluating solutions in said new solution set comprises; determining a method for evaluating solutions based at least in part of said analyzing; evaluating solutions in said new solution set based on said determined method. - View Dependent Claims (16, 17, 18)
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19. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
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a) generating an initial solution set; b) evaluating solutions in said generated solution set in the one or more computers; c) selecting desirable solutions from said generated solution set and saving said selected desirable solutions in the one or more computers; d) creating a model in the one or more computers of said selected desirable solutions; e) mutating a best individual in said generated solution set; f) updating said created model using said mutated best individual; g) generating a new solution set in the one or more computers using said updated model; h) determining if stopping criteria is satisfied; i) if stopping criteria is not satisfied, repeating steps b)-g), wherein said generated new solution set replaces said generated new solution set in step b). - View Dependent Claims (20, 21, 22)
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23. A method for optimizing a solution set in one or more computers, the solution set comprising members representing individual solutions to a problem, the method comprising, not necessarily in the sequence listed:
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a) generating an initial solution set; b) evaluating solutions in said generated solution set in the one or more computers; c) selecting desirable solutions from said generated solution set and saving said selected desirable solutions in the one or more computers; d) creating a model in the one or more computers of said selected desirable solutions; e) mutating a best individual in said generated solution set; f) updating said created model using said mutated best individual; g) generating a new solution set in the one or more computers using said updated model; h) determining if stopping criteria is satisfied; i) if stopping criteria is not satisfied, repeating steps b)-g), wherein said generated new solution set replaces said generated new solution set in step b); wherein said created model comprises a marginal product model (MPM) and wherein said updating said created model comprises updating instance frequencies of the MPM according to building block instances present on said mutated best individual; wherein said updating instance frequencies of the MPM comprises; increasing BB instances frequencies of the mutated individual by s; decreasing the BB instances frequencies of a previous best individual by s; wherein said evaluating solutions uses an s-wise selection.
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Specification