Data Mining Technique With Maintenance of Ancestry Counts

Abstract

Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module for deploying selected individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding in dependence upon their updated fitness estimate. The system maintains the ancestry count for each of the candidate individuals, and may use this information to adjust the competition among the individuals, to adjust the selection of individuals for further procreation, and / or for other purposes.

Description

CROSS-REFERENCE TO OTHER APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 14 / 595,991, filed Jan. 13, 2015, entitled “Data Mining Technique With Maintenance of Ancestry Counts,” which claims priority to U.S. Provisional Application No. 61 / 932,659, filed Jan. 28, 2014, entitled “Data Mining Technique With Maintenance of Ancestry Counts,” which applications are incorporated herein by reference in their entirety.[0002]This application also relates to U.S. patent application Ser. No. 13 / 184,307, filed 15 Jul. 2011, entitled “DATA MINING TECHNIQUE WITH EXPERIENCE-LAYERED GENE POOL,” by Babak Hodjat, Hormoz Shahrzad and Greg S. Hornby, now U.S. Pat. No. 8,909,570, which application is incorporated by reference herein.BACKGROUND[0003]The invention relates generally to data mining, and more particularly, to the use of genetic algorithms to extract useful rules or relationships from a data set for use in controlling systems.[0004]In many environments, a...

AI Technical Summary

Benefits of technology

This patent is about a computer-based system that uses data mining to improve the performance of individuals in a gene pool. The system analyzes information about the individuals and their fitness to determine which ones should be selected for further development. By taking into account the number of times an individual has been through the gene pool, the system can make informed decisions about which individuals to select. This helps to create a more efficient and effective gene pool, leading to better performance overall.

Problems solved by technology

A computer security environment may record a large number of software code examples that have been found to be malicious.

Despite the large quantities of such data, or perhaps because of it, deriving useful knowledge from such data stores can be a daunting task.

A common problem with evolutionary algorithms is that of premature convergence: after some number of evaluations the population converges to local optima and no further improvements are made no matter how much longer the algorithm is run.

When using genetic algorithms to mine a large database, it may not be practical to test each individual against the entire database.

The system therefore rarely if ever knows the true fitness of any individual.

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