Methods and systems for applying genetic operators to determine system conditions

• US 20050118612A1
• Filed: 07/30/2004
• Published: 06/02/2005
• Est. Priority Date: 08/01/2003
• Status: Active Grant

• Current Assignee: Telepathy Labs, Inc.
• Original Assignee: Icosystem Corp.
• Inventors: Anderson, Carl, Budynek, Julien, Scott, John M., Malinchik, Sergey, Bonabeau, Eric
• Granted Patent: US 7,333,960 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

Disclosed are methods, systems, and/or processor program products that include generating a population of genotypes, the genotypes based on at least one stimulus to a system, measuring at least one response of the system upon providing the population of genotypes to at least one model of the system, and, based on the measured at least one response of the system, performing at least one of: (a) applying at least one genetic operator to at least some of the population of genotypes, and iteratively returning to generating a population of genotypes, and (b) associating a condition of the system with at least one of the population of genotypes.

Claims

1. A method, comprising:

generating a population of genotypes, the genotypes based on at least one stimulus to a system,

measuring at least one response of the system upon providing the population of genotypes to at least one model of the system, and,

based on the measured at least one response of the system, performing at least one of;

(a) applying at least one genetic operator to at least some of the population of genotypes, and

iteratively returning to generating a population of genotypes, and

(b) associating a condition of the system with at least one of the population of genotypes.

2. A method according to claim 1, where applying at least one genetic operator includes applying at least one of:

selection, crossover, mutation, deletion, diversity injection, and elitism.

3. A method according to claim 1, where applying at least one genetic operator includes implementing elitism by:

presenting at least two graphical representations to a user, each of the at least two graphical representations associated with at least one genotype in the population and the at least one of the measured responses,

receiving a selection of at least one of the graphical representations, and,

identifying at least one genotype associated with the at least one selected graphical representation,

where returning to generating a population of genotypes comprises including the identified at least one genotype.

4. A method according to claim 1, where applying at least one genetic operator includes implementing elitism by:

comparing the measured at least one response to a measure, and,

based on the comparison, identifying at least one genotype, and,

where returning to generating a population of genotypes comprises including the identified at least one genotype.

5. A method according to claim 1, where associating a condition of the system with at least one of the population of genotypes includes:

comparing the measured at least one response to at least one threshold, and,

determining the condition based on the comparison.

6. A method according to claim 1, where measuring at least one response of the system includes comparing the measured at least one response to a metric.

7. A method according to claim 1, where measuring at least one response of the system includes comparing the measured at least one response to at least one of a fitness function and an objective function.

8. A method according to claim 1, where measuring at least one response of the system includes:

determining that an objective function is mathematically unexpressed,

presenting data based on the measured at least one response of the system to at least one user, and,

receiving at least one input from the at least one user, the at least one input based on the at least one user'"'"'s evaluation of the presented data.

9. A method according to claim 8, where the at least one user input includes at least one of:

a rank of the measured at least one response, a rating of the measured at least one response, one or more fitness values, a selection of the measured at least one response, a selection of a feature of the measured at least one response, a termination of the method, an identification of parents for a genetic algorithm, at least one constraint, a modification of at least one constraint, a modification of at least one genetic operator, and a specification of at least one genetic operator.

10. A method according to claim 8, where the method is terminated based on the at least one user input.

11. A method according to claim 1, where measuring at least one response of the system includes at least one of:

presenting data based on the measured at least one response in parallel, and presenting data based on the measured at least one response in sequential order.

12. A method according to claim 1, where applying at least one genetic operator includes:

ranking the measured at least one response based on a comparison to a metric, and,

applying the at least one genetic operator based on the ranking.

13. A method according to claim 1, where applying at least one genetic operator includes applying at least one constraint to at least one of the genotypes.

14. A method according to claim 13, where applying at least one constraint includes weighting the at least one constraint.

15. A method according to claim 1, where the system condition includes at least one of:

at least one unknown system vulnerability, at least one system error, at least one system defect, at least one system loophole, and at least one system weakness.