紀要論文

遺伝的アルゴリズムとニューラルネットワークによるStrassenの行列積アルゴリズムの自動導出

USING GENETIC ALGORITHMS AND NEURAL NETWORKS AUTOMATIC DERIVATION OF STRASSEN’S MATRIX PRODUCT ALGORITHM

jpn

24368083

https://doi.org/10.15002/00026392

法政大学大学院理工学研究科

法政大学大学院紀要. 理工学研究科編

64

1

4

2023-03-24

Version of Record

Strassen's matrix product algorithm is a fast method for computing matrix products. By using the main principles of this algorithm, the computation of the nth-order matrix product can be significantly reduced. The purpose of this study is to confirm whether Strassen's matrix product algorithm can be automatically derived using Genetic Algorithm and Neural Network. Specifically, we use genetic algorithms to determine the structure of the neural network, including interconnections. Although we could not obtain very practical results for the degree of adaptation, we considered that this was due to the fact that the combination of addition, subtraction, and multiplication was left to the genetic algorithm, which resulted in a different combination of calculations from those that were originally necessary. We should consider the composition of neural networks and increase the number of constraints.

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