Publication detail

Genetic Programming for Source Code Generation to Solve NP-hard Problems

SAFONOV, Y. RAJNOHA, M.

Original Title

Genetic Programming for Source Code Generation to Solve NP-hard Problems

English Title

Genetic Programming for Source Code Generation to Solve NP-hard Problems

Type

journal article

Language

en

Original Abstract

This paper describes the usage of genetic programming method for source code generation with motivation to solve NP-hard problems. Described approach may be used in a wide range of modern applications, whose working principle allows to apply optimization techniques. Proposed method was used to find a potential solution for achieving maximal score while playing a computer game called "Robocode tanks". The main principle of the experiment is based on applying classical evolution approaches on the selected problem in order to implement adaptive machine learning technique. During the training process of presented approach convergence starts and after several cycles of evolution, created tank achieved significantly better final score compared to using a classic programming approach.

English abstract

This paper describes the usage of genetic programming method for source code generation with motivation to solve NP-hard problems. Described approach may be used in a wide range of modern applications, whose working principle allows to apply optimization techniques. Proposed method was used to find a potential solution for achieving maximal score while playing a computer game called "Robocode tanks". The main principle of the experiment is based on applying classical evolution approaches on the selected problem in order to implement adaptive machine learning technique. During the training process of presented approach convergence starts and after several cycles of evolution, created tank achieved significantly better final score compared to using a classic programming approach.

Keywords

genetic programming; evolutionary algoritms; NP hard; code generation; robocode

Released

28.02.2019

Pages from

21

Pages to

27

Pages count

7

URL

BibTex


@article{BUT156735,
  author="Yehor {Safonov} and Martin {Rajnoha}",
  title="Genetic Programming for Source Code Generation to Solve NP-hard Problems",
  annote="This paper describes the usage of genetic programming method for source code generation with motivation to solve NP-hard problems. Described approach may be used in a wide range of modern applications, whose working principle allows to apply optimization techniques. Proposed method was used to find a potential solution for achieving maximal score while playing a computer game called "Robocode tanks". The main principle of the experiment is based on applying classical evolution approaches on the selected problem in order to implement adaptive machine learning technique. During the training process of presented approach convergence starts and after several cycles of evolution, created tank achieved significantly better final score compared to using a classic programming approach.",
  chapter="156735",
  howpublished="online",
  number="1",
  volume="21",
  year="2019",
  month="february",
  pages="21--27",
  type="journal article"
}