Speculative asynchronous sub-population evolutionary computing

• US 10,346,743 B2
• Filed: 01/31/2017
• Issued: 07/09/2019
• Est. Priority Date: 06/30/2011
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Cantin, Jason F.
• Primary Examiner(s): Shmatov, Alexey
• Assistant Examiner(s): Bejcek, II, Robert
• Application Number:
  US15/420,347
• Publication Number:
  US 20170140274A1
• Time in Patent Office: 889 Days
• Field of Search: None
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A tool computes fitness values for a first generation of a first sub-population of a plurality of sub-populations. A population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations. The population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem. The tool determines a speculative ranking of the first generation of the first sub-population prior to the fitness values being computed for all candidate solutions in the first generation of the first sub-population. The tool generates a next generation of the first sub-population based, at least in part, on the speculative ranking prior to completion of computation of the fitness values for the first generation of the first sub-population.

Claims

1. A computer implemented method for speculative evolutionary computing, the method comprising:

receiving, via one or more processors, fitness values for a first generation of a first sub-population of a plurality of sub-populations, wherein a population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations, wherein the population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem;

determining, via at least one of the processors, whether the first generation of the first sub-population does not satisfy a termination criterion for the iterative computing processing;

determining, via at least one of the processors, whether the first generation corresponds to a later iteration of the iterative computing process than a second generation of a second sub-population of the plurality of sub-populations;

determining, via at least one of the processors, whether a difference between the first generation and the second generation does not exceed a termination speculation threshold; and

generating, via at least one of the processors, a third generation of the first sub-population responsive to a determination that the difference between the first generation and the second generation does not exceed the termination speculation threshold, wherein the generating the third generation of the first sub-population based, at least in part, on the fitness values.

2. The method of claim 1, wherein said generating the third generation of the first sub-population includes generating the third generation in accordance with one or more evolutionary computing techniques implemented in accordance with the evolutionary algorithm.

3. The method of claim 2, wherein the one or more evolutionary computing techniques comprise at least one of crossover, mutation, and reproduction.

4. The method of claim 1, further comprising dividing, via at least one of the processors, the population of candidate solutions for the optimization problem into the plurality of sub-populations of candidate solutions and assigning the plurality of sub-populations to computing resources.

5. The method of claim 1, wherein determining whether the first generation corresponds to the later iteration of the iterative computing process than the second generation of the second sub-population of the plurality of sub-populations comprises:

accessing generation tracking data based on a first computing resource identifier and a second computing resource identifier,

wherein the generation tracking data indicates an iteration of the iterative computing process for each of a plurality of computing resources,

wherein the first computing resource identifier identifies a first computing resource of the plurality of computing resources and the second computing resource identifier identifies a second computing resource of the plurality of computing resources,

wherein the first computing resource is associated with the first sub-population and the second computing resource is associated with the second sub-population; and

comparing a first iteration indicated for the first computing resource and a second iteration indicated for the second computing resource identifier.

6. The method of claim 1, further comprising:

determining, via at least one of the processors, that the third generation of the first sub-population should receive a migrant candidate solution from a neighboring one of the plurality of sub-populations;

determining, via at least one of the processors, that the migrant candidate solution from the neighboring one of the plurality of sub-populations is not available;

creating, via at least one of the processors, a speculative migrant candidate solution; and

inserting, via at least one of the processors, the speculative migrant candidate solution into the third generation of the first sub-population of candidate solutions.

7. The method of claim 6, further comprising determining that a condition for speculatively creating the migrant candidate solution is satisfied before said creating the speculative migrant candidate solution.

8. The method of claim 6, further comprising tagging the speculative migrant candidate solution to indicate speculative.