PL EN


2016 | 5 | 3 | 377-388
Article title

IMPLEMENTING EVOLUTIONARY ALGORITHM INTO TRAINING SINGLE-LAYER ARTIFICIAL NEURAL NETWORK IN CLASSIFICATION TASK

Content
Title variants
Languages of publication
EN
Abstracts
EN
The article proposes implementing a modified version of genetic algorithm in a neural network, what in literature is known as “evolutionary algorithm” or “evolutionary programming”. An Evolutionary Algorithm is a probabilistic algorithm that works in a set of weight variability of neurons and seeks the optimal value solution within a population of individuals, avoiding the local maximum. For chromosomes the real value variables and matrix structure are proposed to a single-layer neural network. Particular emphasis is put on mutation and crossover algorithms. What is also important in both genetic and evolutionary algorithms is the selection process. In the calculation example, the implementation of theoretical considerations to a classification task is demonstrated.
Year
Volume
5
Issue
3
Pages
377-388
Physical description
Dates
published
2016
Contributors
  • Faculty of Computer Science, Vistula University (AFiB Vistula)
References
  • A.E. Eiben, J.E. Smith: Introduction to Evolutionary Computing, Second Edition, Springer 2003, 2015.
  • Michalewicz Z.: Genetic Algorithm + Data Structure = Evolutionary Programs, Springer – Verlag Berlin Haidelberg 1996.
  • Montana DJ, Davis L,: "Training Feedforward Neural Network Using Genetic Algorithms. Proceedings of the 1989 International Join Conference on Artificial Intelligence", Morgan Kaufmann Publishers, San Mateo, CA, 1989.
  • David E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison- Veslay Publishing Company, Inc. 1989.
  • Xinjie Yu, Mitsuo Gen: Introduction to Evolutionary Algorithm, Springer London 2010.
Document Type
Publication order reference
Identifiers
ISSN
2084-5537
YADDA identifier
bwmeta1.element.desklight-5c8f7104-522c-48f6-9a7b-a1ba2e4af9fc
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.