Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An arrangement of processing units, for use with a data mining training database containing training data, comprising: a first processing unit having a first processor and a first memory, the first memory storing a first local pool of candidate individuals, each of the candidate individuals in the first local pool identifying at least one classification rule and further identifying a respective fitness estimate centric to the first processing unit; and a second processing unit disposed down-chain of the first processing unit, the second processing unit having a second processor and a second memory, the second memory storing a second local pool of candidate individuals, each of the candidate individuals in the second local pool identifying at least one classification rule and further identifying a respective fitness estimate centric to the second processing unit, wherein the first processor is configured to: store in the first memory individuals for evaluation against portions of the training data, delegate, to the second processing unit, evaluation of individuals from the first local pool of candidate individuals, update the fitness estimates, centric to the first processing unit, for selected ones of individuals received back from the second processing unit after testing, in dependence upon results of such testing, and select individuals for discarding from the first local pool, in dependence upon their fitness estimates relative to the fitness estimates of the other individuals within the first local pool, and wherein the second processor is configured to: store in the second local pool of candidate individuals, individuals received by the second processing unit from the first processing unit for evaluation, test individuals from the second local pool of candidate individuals against a portion of the training data, update the fitness estimates, centric to the second processing unit, for selected ones of individuals tested, in dependence upon the results of such testing, select individuals for discarding from the second local pool, in dependence upon their novelty relative to other individuals in the second local pool, and report to the first processing unit, individuals from the second local pool and not selected for discarding, in conjunction with the individuals' fitness estimates, centric to the second processing unit. 2. The arrangement of claim 1, wherein selecting individuals for discarding from the second local pool in dependence upon their novelty relative to other individuals in the second local pool, is performed further in dependence upon their fitness estimates relative to other individuals in the second local pool. 3. The arrangement of claim 1, wherein testing individuals from the second local pool of candidate individuals against a portion of the training data, includes recording, for each individual tested, an identification of the behavior of the individual when tested against the portion of the training data, and wherein selecting individuals for discarding from the second local pool in dependence upon their novelty relative to other individuals in the second local pool comprises iteratively: identifying, among the individuals in at least a non-null subset of the individuals in the second local pool, the pair whose recorded behavior is most similar; and selecting one individual of the identified pair for discarding. 4. The arrangement of claim 3, wherein selecting one individual of the identified pair for discarding comprises selecting for discarding the individual of the identified pair having a lower fitness estimate, centric to the second processing unit. 5. The arrangement of claim 1, wherein the second processor is further configured to add into the second local pool individuals developed by reproduction in dependence upon parent individuals remaining in the second local pool after the selecting individuals for discarding from the second local pool. 6. A computer system which transmits toward a second processing unit, software which, when executed by a second processor performs: storing in a second local pool of candidate individuals, individuals received by the second processing unit from a first processing unit for evaluation, testing individuals from the second local pool of candidate individuals against a portion of training data, updating fitness estimates, centric to the second processing unit, for selected ones of individuals tested, in dependence upon results of such testing, selecting individuals for discarding from the second local pool, in dependence upon their novelty relative to other individuals in the second local pool, and reporting toward the first processing unit, individuals from the second local pool and not selected for discarding, in conjunction with the individuals' fitness estimates, centric to the second processing unit. 7. The computer system of claim 6, which further transmits toward the first processing unit having a first processor, software which, when executed by the first processor performs: storing in a first memory individuals for evaluation against portions of the training data, delegating, to the second processing unit, evaluation of individuals from a first local pool of candidate individuals, updating fitness estimates, centric to the first processing unit, for selected ones of individuals received back from the second processing unit after testing, in dependence upon the results of such testing, and selecting individuals for discarding from the first local pool, in dependence upon their fitness estimates relative to the fitness estimates of the other individuals within the first local pool. 8. The computer system of claim 6, wherein selecting individuals for discarding from the second local pool in dependence upon their novelty relative to other individuals in the second local pool, is performed further in dependence upon their fitness estimates relative to other individuals in the second local pool. 9. The computer system of claim 6, wherein testing individuals from the second local pool of candidate individuals against a portion of the training data, includes recording, for each individual tested, an identification of the behavior of the individual when tested against the portion of the training data, and wherein selecting individuals for discarding from the second local pool in dependence upon their novelty relative to other individuals in the second local pool comprises iteratively: identifying, among the individuals in at least a non-null subset of the individuals in the second local pool, the pair whose recorded behavior is most similar; and selecting one individual of the identified pair for discarding. 10. The computer system of claim 9, wherein selecting one individual of the identified pair for discarding comprises selecting for discarding the individual of the identified pair having a lower fitness estimate, centric to the second processing unit. 11. The computer system of claim 6, wherein the second processor is further configured to add into the second local pool individuals developed by reproduction in dependence upon parent individuals remaining in the second local pool after the selecting individuals for discarding from the second local pool. 12. An evolutionary engine for use with a data mining training database containing training data, and for use further with an evolutionary coordinator having a pool of candidate individuals stored accessibly thereto, the evolutionary engine comprising: a processor and a memory, the memory storing accessibly to the processor a local pool of candidate individuals, each of the candidate individuals in the local pool identifying at least one classification rule and further identifying a respective fitness estimate centric to the evolutionary engine, wherein the processor is configured to: store in the local pool, individuals received by the evolutionary engine from the evolutionary coordinator for evaluation, test individuals from the local pool against a portion of the training data, update the fitness estimates, centric to the evolutionary engine, for selected ones of individuals tested, in dependence upon the results of such testing, select individuals for discarding from the local pool, in dependence upon their novelty relative to other individuals in the local pool, and report toward the evolutionary coordinator individuals from the local pool and not selected for discarding, in conjunction with the individuals' fitness estimates centric to the evolutionary coordinator. 13. The evolutionary engine of claim 12, wherein in selecting individuals for discarding from the local pool in dependence upon their novelty relative to other individuals in the local pool, the processor is configured to select the individuals for discarding further in dependence upon their fitness estimates relative to other individuals in the local pool. 14. The evolutionary engine of claim 12, wherein in testing individuals from the local pool of candidate individuals against a portion of the training data, the processor is configured to record, for each individual tested, an identification of the behavior of the individual when tested against the portion of the training data, and wherein in selecting individuals for discarding from the local pool in dependence upon their novelty relative to other individuals in the local pool the processor is configured to iteratively: identify, among the individuals in at least a non-null subset of the individuals in the local pool, the pair whose recorded behavior is most similar; and select one individual of the identified pair for discarding. 15. The evolutionary engine of claim 12, wherein in selecting one individual of the identified pair for discarding the processor is configured to select for discarding the individual of the identified pair having a lower fitness estimate, centric to the evolutionary engine. 16. The evolutionary engine of claim 12, wherein the processor is further configured to add into the local pool individuals developed by reproduction in dependence upon parent individuals remaining in the local pool after the selecting individuals for discarding from the local pool. 17. The evolutionary engine of claim 12, wherein in selecting individuals for reporting, the processor is configured to select the individuals for reporting in dependence upon the fitness estimates of the individuals centric to the evolutionary engine, from among the individuals in the local pool not yet selected for discarding.