Methods for efficient solution set optimization
First Claim
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1. A method for optimizing a solution set comprising the steps of, not necessarily in the sequence listed:
- a) creating an initial solution set;
b) identifying a desirable portion of said initial solution set using a fitness calculator;
c) creating a model that is representative of said desirable portion;
d) using said model to create a surrogate fitness estimator that is computationally less expensive than said fitness calculator;
e) generating new solutions;
f) replacing at least a portion of said initial solution set with said new solutions to create a new solution set; and
g) evaluating at least a portion of said new solution set with said fitness surrogate estimator to identify a new desirable portion.
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Abstract
A method for optimizing a solution set comprises the steps of generating an initial solution set, identifying a desirable portion of the initial solution set using a fitness calculator, using the desirable portion to create a surrogate fitness model that is computationally less expensive than the fitness calculator, generating new solutions, replacing at least a portion of the initial solution set with the new solutions to create a second solution set, and evaluating at least a portion of the second solution set with the fitness surrogate model to identify a second desirable portion.
81 Citations
20 Claims
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1. A method for optimizing a solution set comprising the steps of, not necessarily in the sequence listed:
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a) creating an initial solution set;
b) identifying a desirable portion of said initial solution set using a fitness calculator;
c) creating a model that is representative of said desirable portion;
d) using said model to create a surrogate fitness estimator that is computationally less expensive than said fitness calculator;
e) generating new solutions;
f) replacing at least a portion of said initial solution set with said new solutions to create a new solution set; and
g) evaluating at least a portion of said new solution set with said fitness surrogate estimator to identify a new desirable portion. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer program product useful to optimize a solution set, the computer program product comprising computer readable instructions stored on a computer readable memory that when executed by one or more computers cause the one or more computers to perform the following steps, not necessarily in the sequence listed:
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a) generate an initial solution set;
b) identify a desirable portion of said initial solution set using a fitness calculator;
c) use said desirable portion to create a model configured to predict other promising solutions, said probabilistic model including a plurality of variables at least some of which interact with one another;
d) use said interactions between said variables to create a surrogate fitness estimator that is computationally less expensive than said fitness calculator;
e) generate new solutions using said probabilistic model;
f) replace at least a portion of said initial solution set with said new solutions to create a new solution set; and
g) evaluate X % of said new solution set using said fitness surrogate estimator and evaluate (100-X) % of said new solution set using said fitness calculator to identify a new desirable portion, where X is between about 75 and 100. - View Dependent Claims (19)
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20. A method for optimizing a solution set comprising the steps of, not necessarily in the sequence listed:
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a) creating an initial solution set;
b) identifying a desirable portion of said initial solution set using a fitness calculator, use of said fitness calculator resulting in fitness calculation data points;
c) storing said fitness calculation data points;
d) using said desirable portion to create a model configured to predict other desirable solutions, said probabilistic model including a plurality of variables at least some of which interact with one another;
e) using said interaction of said variables in said model and said fitness calculation data points to create a surrogate fitness estimator that is computationally less expensive than said fitness calculator;
f) generating new solutions;
g) replacing at least a portion of said initial solution set with said new solutions to create a new solution set; and
h) evaluating X % of said new solution set with said fitness surrogate estimator and the remaining (100-X) % of said second solution set using said fitness calculator to identify a new desirable portion, where X is between about 75 and about 100; and
,i) determining whether completion criteria are satisfied and if not repeating steps d)-h) until said completion criteria are completed, the step of repeating including replacing said desirable portion in step d) with said new desirable portion and replacing said initial solution set in step g) with said new solution set
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Specification