Call for Paper - January 2019 Edition
IJCA solicits original research papers for the January 2019 Edition. Last date of manuscript submission is December 20, 2018. Read More

Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 2 - Article 2
Year of Publication: 2011
Karam M. Eghnam
Sulieman Bani-Ahmad
Alaa F. Sheta

Karam M Eghnam, Sulieman Bani-Ahmad and Alaa F Sheta. Article: Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model. International Journal of Computer Applications 19(2):8-12, April 2011. Full text available. BibTeX

	author = {Karam M. Eghnam and Sulieman Bani-Ahmad and Alaa F. Sheta},
	title = {Article: Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {19},
	number = {2},
	pages = {8-12},
	month = {April},
	note = {Full text available}


The Capelin stock in the Barents Sea is the largest in the world. It maintained a fishery with annual catches of up to 3 million tons. The Capelin stock problem has an impact in fish stock development. In this paper, the stock prediction problem of the Barents Sea capelin is attacked using Artificial Neural Network (ANNs) and Multiple Linear model Regression (MLR) model. The weights of ANNs are adapted using the Genetic Algorithm (GA).The models are compared against each other and empirical work has shown that the ANN-GA model can have better overall accuracy over (MLR). It performs 21% over MLR model.


  • Seafood: Sf/ May 12, 2005.
  • H.Yndestad, and A. Stene (2002). System dynamics of the Barents Sea capelin. ICES Journal of Marine Science, pp. 1155–1166
  • K. Eghnam and Alaa F. Sheta (2007), Training artificial neural networks using genetic algorithms to predict the price of the general index for Amman Stock Exchange, In the Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference, DePaul University, Chicago, IL, USA, pp. 1-7.
  • Vilhjálmsson, H. (1994). The Icelandic Capelin Stock. Journal of Marine Research Institute, Reykjavik, Vol. XIII, No. 1.
  • K. Richard, Belew, J. McInerny, and N. Schraudolph (1990). Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. Technical Report CS90–174, UCSD (La Jolla).
  • Anon, (1999). Preliminary report of the international 0-group survey in the Barents Sea and adjacent waters in August- September. ICES Council Meeting.
  • B. Anon, Ressursoversikten. (1999). Bergen, Norway: Institute of Marine Research.
  • H. Holland (1975). Adaptation in natural and artificial systems. University of Michigan, pp.183.
  • H. Gjosaeter, H. Loeng (1987). Growth of the Barents Sea capelin, Mallotus villosus, in relation to climate. Environmental Biology of Fishes, Volume 20, Number 4, 293-300, DOI: 10.1007/BF00005300.
  • Darrell Whitley. Applying Genetic Algorithms to Neural Network Problems: A Preliminary Report.
  • D. Whitley and T. Hanson (1989). The Genitor Algorithm: Using Genetic Algorithms to Optimize Neural Networks. Technical Report, pp. S-89-107, Colorado State University.
  • D. Whitley, T. Starkweather, and C. Bogart (1989). Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity. Technical Report, pp. 89-117, Colorado State University.
  • D.Whitley and T. Hanson (1989). The genetic algorithm: Using genetic algorithms to optimize neural networks. Technical Report, pp. 89-107, University of Colorado state.
  • N.J. Radcliffe (1991). Genetic Set Recombination and its Application to Neural Network Topology Optimization. Proceedings of the 4rth International Conference on Genetic Algorithms. pp. 222–229.
  • N. J. Radcliffe (1990). Genetic Neural Networks on MIMD Computers, PhD thesis, University of Edinburgh, UK.
  • A.S Chen, M.T. Leung, and H. Daouk (2003). Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index. Computers and Operations Research pp. 901-923.
  • K. Eghnam, A. Sheta, S. Bani-Ahmad. (2011). Optimal Weight Selection of ANN to Predict the Price of the General Index for Amman Stock Exchange. Journal of Computing, Volume 3, Issue 3, March 2011, ISSN 2151-9617.