METHOD FOR IMPROVING NEURAL NETWORK ARCHITECTURES USING EVOLUTIONARY ALGORITHMS
First Claim
1. A method for enabling a determination of a preferred neural network architecture, the method comprising:
- enabling an encoding of each chromosome of a plurality of chromosomes, each chromosome being associated with each neural network of a plurality of neural networks, each chromosome including;
a first parameter that defines an initial condition of the associated neural network, and a second parameter that defines an architectural feature of the associated neural network, enabling an evaluation of each neural network of the plurality of neural networks based on the initial condition and the architectural feature of each neural network, to provide a measure of effectiveness associated with each chromosome, and enabling a selection of the preferred neural network architecture based on the measure of effectiveness associated with each chromosome.
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Abstract
sets or evaluation on sets among the evaluated neural networks may cause potentially worthwhile architectures to be rejected prematurely, obviating the advantages realizable by a directed trial and error process. It is an object of this invention to provide a method for improving neural network architectures via an evolutionary algorithm that reduces the adverse effects of the noise that is introduced by the network initialization process. It is a further object of this invention to reduce the noise that is introduced by the network initialization process. It is a further object of this invention to provide an optimized network initialization process. It is a further object of this invention to reduce the noise that is introduced by the use of randomly selected training or evaluation input sets.
These objects and others are achieved by including parameters that affect the initialization of a neural network architecture within the encoding that is used by an evolutionary algorithm to optimize the neural network architecture. The example initialization parameters include an encoding that determines the initial nodal weights used in each architecture at the commencement of the training cycle. By including the initialization parameters within the encoding used by the evolutionary algorithm, the initialization parameters that have a positive effect on the performance of the resultant evolved network architecture are propagated and potentially improved from
17 Citations
15 Claims
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1. A method for enabling a determination of a preferred neural network architecture, the method comprising:
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enabling an encoding of each chromosome of a plurality of chromosomes, each chromosome being associated with each neural network of a plurality of neural networks, each chromosome including;
a first parameter that defines an initial condition of the associated neural network, and a second parameter that defines an architectural feature of the associated neural network, enabling an evaluation of each neural network of the plurality of neural networks based on the initial condition and the architectural feature of each neural network, to provide a measure of effectiveness associated with each chromosome, and enabling a selection of the preferred neural network architecture based on the measure of effectiveness associated with each chromosome. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for enabling a determination of at least one preferred neural network architecture, the method comprising:
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enabling a definition of a plurality of first generation network architectures, enabling a selection of a first random set of training input vectors, enabling a training of each network architecture of the plurality of first generation network architectures based on the first random set of training input vectors to form a corresponding plurality of trained first generation network architectures, enabling an evaluation of each trained first generation network architecture of the plurality of trained first generation network architectures to provide a measure of effectiveness associated with each trained first generation network architecture, enabling a definition of a plurality of second generation network architectures, based on the measure of effectiveness associated with each trained first generation network architecture, enabling a selection of a second random set of training input vectors, enabling a training of each network architecture of the plurality of second generation network architectures based on the second random set of training input vectors to form a corresponding plurality of trained second generation network architectures, enabling an evaluation of each trained second generation network architecture of the plurality of trained second generation network architectures to provide a measure of effectiveness associated with each trained second generation network architecture, enabling a selection of the at least one preferred neural network architecture based on the measure of effectiveness associated with each trained second generation network architecture.
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10. A method for enabling a determination of at least one preferred neural network architecture, the method comprising:
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enabling a definition of a plurality of first generation network architectures, enabling a training of each network architecture of the plurality of first generation network architectures to form a corresponding plurality of trained first generation network architectures, enabling a selection of a first random set of evaluation input vectors, enabling an evaluation of each trained first generation network architecture of the plurality of trained first generation network architectures based on the first random set of evaluation input vectors to provide a measure of effectiveness associated with each trained first generation network architecture, enabling a definition of a plurality of second generation network architectures based on the measure of effectiveness associated with each trained first generation network architecture, enabling a training of each network architecture of the plurality of second generation network architectures to form a corresponding plurality of trained second generation network architectures, enabling a selection of a second random set of evaluation input vectors, enabling an evaluation of each trained second generation network architecture of the plurality of trained second generation network architectures based on the second random set of evaluation input vectors to provide a measure of effectiveness associated with each trained second generation network architecture, enabling a selection of the at least one preferred neural network architecture based on the measure of effectiveness associated with each trained second generation network architecture.
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11. A system comprising:
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a neural network device comprising a neural network that provides an output vector in response to an input vector that is applied to the neural network, the output vector being dependent upon an initial condition of the neural network, and an evolutionary algorithm device, operably coupled to the neural network device, that is configured to provide;
a network architecture parameter that affects the neural network and a network initialization parameter that affects the initial condition of the neural network based on an evaluation of an effectiveness of an other output vector provided by the neural network device. - View Dependent Claims (12, 13, 14, 15)
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Specification