Article

A developmental model of neural computation using cartesian genetic programming

Authors:

Gul Muhammad Khan,

Julian F. Miller,

David M. HallidayAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

Pages 2535 - 2542

https://doi.org/10.1145/1274000.1274022

Published: 07 July 2007 Publication History

Abstract

The brain has long been seen as a powerful analogy from which novel computational techniques could be devised. However, most artificial neural network approaches have ignored the genetic basis of neural functions. In this paper we describe a radically different approach. We have devised a compartmental model of a neuron as a collection of seven chromosomes encoding distinct computational functions representing aspects of real neurons. This model allows neurons, dendrites, and axon branches to grow, die and change while solving a computational problem. This also causes the synaptic morphology to change and affect the information processing. Since the appropriate computational equivalent functions of neural computation are unknown, we have used a form of genetic programming known as Cartesian Genetic Programming (CGP) to obtain these functions. We have evaluated the learning potential of this system in the context of solving a well known agent based learning scenario, known as wumpus world and obtained promising results.

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Cited By

Khan GDurr-e-Nayab (2018)Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural NetworksArtificial Neural Networks and Machine Learning – ICANN 201810.1007/978-3-030-01424-7_20(199-207)Online publication date: 27-Sep-2018
https://doi.org/10.1007/978-3-030-01424-7_20
Turner AMiller JAlba E(2013)Cartesian genetic programming encoded artificial neural networksProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463484(1005-1012)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463484
Khan GNayab DMahmud SZafar H(2013)Evolving Dynamic Forecasting Model for Foreign Currency Exchange Rates Using Plastic Neural NetworksProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.99(15-20)Online publication date: 4-Dec-2013
https://dl.acm.org/doi/10.1109/ICMLA.2013.99
Show More Cited By

Index Terms

A developmental model of neural computation using cartesian genetic programming
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

July 2007

1450 pages

ISBN:9781595936981

DOI:10.1145/1274000

Program Chairs:
Peter A. N. Bosman
Centre for Mathematics & Computer Science, The Netherlands
,
Tina Yu
Memorial University of Newfoundland, Canada
,
Anikó Ekárt
Aston University, UK

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 July 2007

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July 7 - 11, 2007

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Khan GDurr-e-Nayab (2018)Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural NetworksArtificial Neural Networks and Machine Learning – ICANN 201810.1007/978-3-030-01424-7_20(199-207)Online publication date: 27-Sep-2018
https://doi.org/10.1007/978-3-030-01424-7_20
Turner AMiller JAlba E(2013)Cartesian genetic programming encoded artificial neural networksProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463484(1005-1012)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463484
Khan GNayab DMahmud SZafar H(2013)Evolving Dynamic Forecasting Model for Foreign Currency Exchange Rates Using Plastic Neural NetworksProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.99(15-20)Online publication date: 4-Dec-2013
https://dl.acm.org/doi/10.1109/ICMLA.2013.99
Khan GMiller JHalliday D(2009)In search of intelligent genes: The cartesian genetic programming computational neuron (CGPCN)2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982997(574-581)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4982997

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