Evolution and learning in neural networks: the number and distribution of learning trials affect the rate of evolution
First Claim
1. In a neural network system for use in application tasks including optical character recognition techniques, an evolutionary evaluation and learning method comprising the steps of:
- forming plural genetic representations of an application task including recognizing a plurality of characters where each character has a bit configuration specifying initial connection strengths between filter lays and category units,converting said plural genetic representations to neural networks,assigning to each network i, k(i) learning trails, chosen randomly between zero and 2K, andtraining each neural network to optimally perform the application task including optical character recognition where the number of learning trials per network is randomly chosen.
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Abstract
The present invention relates to the interrelationships between nature (as mediated by evolution and genetic algorithms) and nurture (as mediated by gradient-descent supervised learning) in a population of neural networks for pattern recognition. The Baldwin effect is demonstrated that learning can change the rate of evolution of the population'"'"'s genome - a "pseudo-Lamarkian" process, in which information learned is ultimately encoded in the genome by a purely Darwinian process. Selectivity is shown for this effect: too much learning or too little learning in each generation leads to slow evolution of the genome, whereas an intermediate amount leads to most rapid evolution. For a given number of learning trials throughout a population, the most rapid evolution occurs if different individuals each receive a different number of learning trials, rather than the same number. Because all biological networks possess structure due to evolution, it is important that such interactions between learning and evolution be understood. Hybrid systems can take advantage both of gradient descents (learning) and large jumps (genetic algorithms) in very complicated energy landscapes and hence may play an increasingly important role in the design of artificial neural systems.
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Citations
10 Claims
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1. In a neural network system for use in application tasks including optical character recognition techniques, an evolutionary evaluation and learning method comprising the steps of:
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forming plural genetic representations of an application task including recognizing a plurality of characters where each character has a bit configuration specifying initial connection strengths between filter lays and category units, converting said plural genetic representations to neural networks, assigning to each network i, k(i) learning trails, chosen randomly between zero and 2K, and training each neural network to optimally perform the application task including optical character recognition where the number of learning trials per network is randomly chosen. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. In a neural network system for use in application tasks, an evolutionary evaluation and learning method comprising the steps of:
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forming a genetic representation of an application task having a bit configuration specifying initial connection strengths between filter layers and category units, converting said genetic representation to neural networks, assigning to each network i, k(i) learning trials, chosen randomly between zero and 2K, and training each neural network to optimally perform the application task where the number of learning trials per network is randomly chosen.
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Specification