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- W2035584430 abstract "This paper presents an evolutionary algorithm for optimal mapping of the backpropagation learning algorithm onto a parallel heterogeneous processor network. Training-set parallelism is used as the paradigm for parallelizing the backpropagation algorithm, and the processor network is a heterogeneous array of transputers connected in a pipelined ring topology. It is known from earlier studies that finding the optimal mapping (i.e. optimal allocation of training patterns among the processors to minimize the time for a training epoch) involves solving a linear Mixed Integer Programming (MIP) problem. Solving the MIP using the traditional Branch and Bound (B&B) method takes a large amount of computing time. Approaches based on evolutionary algorithms are then investigated as alternatives to the branch and bound method to solve the pattern allocation problem. It is found that a conventional genetic algorithms (GAs) search does not perform as well as the B&B in terms of the quality of solution and the search tim..." @default.
- W2035584430 created "2016-06-24" @default.
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- W2035584430 date "1999-01-01" @default.
- W2035584430 modified "2023-09-24" @default.
- W2035584430 title "An evolutionary algorithm for parallel mapping of backpropagation learning on heterogeneous processor networks" @default.
- W2035584430 doi "https://doi.org/10.1080/002077299292452" @default.
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