IEICE TRANSACTIONS on Information
A Constructive Compound Neural Networks. II Application to Artificial Life in a Competitive Environment
Summary :
We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.
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- IEICE TRANSACTIONS on Information Vol.E83-D No.4 pp.845-856
- Publication Date
- 2000/04/25
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- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Cognitive Science
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Jianjun YAN, Naoyuki TOKUDA, Juichi MIYAMICHI, "A Constructive Compound Neural Networks. II Application to Artificial Life in a Competitive Environment" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 4, pp. 845-856, April 2000, doi: .
Abstract: We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_4_845/_p
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@ARTICLE{e83-d_4_845,
author={Jianjun YAN, Naoyuki TOKUDA, Juichi MIYAMICHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Constructive Compound Neural Networks. II Application to Artificial Life in a Competitive Environment},
year={2000},
volume={E83-D},
number={4},
pages={845-856},
abstract={We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.},
keywords={},
doi={},
ISSN={},
month={April},}
Copy
TY - JOUR
TI - A Constructive Compound Neural Networks. II Application to Artificial Life in a Competitive Environment
T2 - IEICE TRANSACTIONS on Information
SP - 845
EP - 856
AU - Jianjun YAN
AU - Naoyuki TOKUDA
AU - Juichi MIYAMICHI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2000
AB - We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.
ER -
English
