Recognition of blue-green algae in lakes using distributive genetic algorithm-based neural networks

Authors:
Zhihong Yao;Minrui Fei;Kang Li;Hainan Kong;Bo Zhao
Affiliations:
School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, PR China and School of Electronic Information and Electrical Engineering, Shanghai JiaotongUniversity, Sha ...;School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, PR China;School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, UK;Environment School, Shanghai Jiaotong University, Shanghai 200030, PR China;Management School, Shanghai Shanda University, Shanghai 201209, PR China
Venue:
Neurocomputing
Year:
2007

Citing 4
Cited 1

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in Digital Mammograms

Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Adapting operator settings in genetic algorithms

Evolutionary Computation
Fuzzy neural networks-based quality prediction system for sintering process

IEEE Transactions on Fuzzy Systems
Parallel nonlinear optimization techniques for training neural networks

IEEE Transactions on Neural Networks

Study on relationship between cyanobacteria blooms and chemical biological factors in a shallow temperate zone lake

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes a distributive genetic algorithm for the learning of neural networks (DGANN). To tackle several well-known problems for conventional genetic algorithms (GAs), a synergetic multi-operator multi-population mechanism is developed, incorporating an @a transformation crossover operator and mixed-crossover operators. The proposed algorithm is applied to both benchmark numerical examples and pattern recognition of blue-green algae in lakes. Experimental results confirm that the proposed algorithm is superior to conventional GAs in terms of the convergence speed and solution precision, and is also capable of generating neural networks with significantly improved generalization performance.