Publication detail

GPAM: Genetic Programming with Associative Memory

JŮZA, T. SEKANINA, L.

Original Title

GPAM: Genetic Programming with Associative Memory

Type

conference paper

Language

English

Original Abstract

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. 

Keywords

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

Authors

JŮZA, T.; SEKANINA, L.

Released

5. 4. 2023

Publisher

Springer Nature Switzerland AG

Location

Cham

ISBN

978-3-031-29572-0

Book

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

Edition

LNCS

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

13986

Number

3

State

Federal Republic of Germany

Pages from

68

Pages to

83

Pages count

16

BibTex

@inproceedings{BUT185128,
  author="Tadeáš {Jůza} and Lukáš {Sekanina}",
  title="GPAM: Genetic Programming with Associative Memory",
  booktitle="26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar",
  year="2023",
  series="LNCS",
  journal="Lecture Notes in Computer Science",
  volume="13986",
  number="3",
  pages="68--83",
  publisher="Springer Nature Switzerland AG",
  address="Cham",
  doi="10.1007/978-3-031-29573-7\{_}5",
  isbn="978-3-031-29572-0",
  issn="0302-9743"
}