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Method and system using global and local catastrophes across sub-populations in parallel evolutionary computing

A group, local technology, applied in the field of evolutionary computing, can solve the problem of premature convergence of the execution instance of the evolutionary algorithm

Inactive Publication Date: 2013-09-11
IBM CORP
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0004] Execution instances of evolutionary algorithms may converge prematurely

Method used

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  • Method and system using global and local catastrophes across sub-populations in parallel evolutionary computing
  • Method and system using global and local catastrophes across sub-populations in parallel evolutionary computing
  • Method and system using global and local catastrophes across sub-populations in parallel evolutionary computing

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Embodiment Construction

[0014] The following description includes example systems, methods, techniques, instruction sequences and computer program products that implement techniques of the inventive subject matter. However, it is understood that the described embodiments may be practiced without these specific details. For example, although the examples refer to machines, embodiments of the inventive subject matter can be practiced in a virtualized environment. For example, subpopulations can be assigned to different virtual machines supported by a single machine. As another example, subpopulations can be assigned to different cores in a multi-core environment. In other instances, well-known instruction instances, protocols, structures and techniques have not been shown in detail in order not to obscure the description.

[0015] the term

[0016] The literature on evolutionary computation uses a large variety of terms. In some cases, terms are used ambiguously. The genetic algorithm literature ...

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Abstract

The invention provides a method and a system using global and local catastrophes across sub-populations in parallel evolutionary computing. A parallel genetic algorithm computing process tracks forward progress of a first sub-population across generations thereof. The first sub-population is one of a plurality of sub-populations that form a population of candidate solutions to an optimization problem. At a current generation of the first sub-population, it is determined that forward progress of the first sub-population fails a set of one or more forward progress criteria. In response to determining that the forward progress of the first sub-population fails the set of one or more forward progress criteria at the current generation, a local catastrophe is invoked on the current generation of the first sub-population. The first sub-population is re-populated after the local catastrophe is invoked. The first sub-population is re-established after re-populating while constraining migration to the first sub-population.

Description

technical field [0001] Embodiments of the inventive subject matter relate generally to the field of evolutionary computing, and more particularly to the use of local and global mutations in evolutionary computing. Background technique [0002] Software tools apply metaheuristic optimization algorithms to solve optimization problems. Examples of meta-heuristic optimization algorithms include evolutionary algorithms (eg, genetic algorithms, differential evolution), ant colony optimization algorithms, simulated annealing algorithms, and the like. [0003] Evolutionary algorithms use techniques loosely based on Darwinian evolution and biological mechanisms to develop solutions to design problems. Software tools implementing evolutionary algorithms start with a population of randomly generated solutions and iteratively use the principles of sex recombination, crossover, mutation, and Darwinian natural selection to create new, more suitable solutions in successive generations. E...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
CPCG06N3/126G06N3/12
Inventor J·F·坎汀
Owner IBM CORP
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