GPAM: Genetic Programming with Associative Memory

conference paper

English

We focus on the evolutionary design of programs capable of capturing more randomness and outliers in the input data set than the standard genetic programming (GP)-based methods typically allow. We propose Genetic Programming with Associative Memory (GPAM) -- a GP-based system for symbolic regression which can utilize a small associative memory to store various data points to better approximate the original data set. The method is evaluated on five standard benchmarks in which a certain number of data points is replaced by randomly generated values. In another case study, GPAM is used as an on-chip generator capable of approximating the weights for a convolutional neural network (CNN) to reduce the access to an external weight memory. Using Cartesian genetic programming (CGP), we evolved expression-memory pairs that can generate weights of a single CNN layer.  If the associative memory contains 10% of the original weights, the weight generator evolved for a convolutional layer can approximate the original weights such that the CNN utilizing the generated weights shows less than a 1% drop in the classification accuracy on the MNIST data set. 

Genetic programming, Associative memory, Neural network, Weight compression, Symbolic regression

JŮZA, T.; SEKANINA, L.

5. 4. 2023

Springer Nature Switzerland AG

Cham

978-3-031-29572-0

26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar

LNCS

0302-9743

Lecture Notes in Computer Science

13986

3

Federal Republic of Germany

68

83

16