Signal representation using adaptive normalized Gaussian functions
Signal Processing
Journal of Computer and System Sciences
Evolutionary-based methods for adaptive signal representation
Signal Processing
Theoretical Computer Science - Natural computing
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
Fundamenta Informaticae
A membrane algorithm for the min storage problem
WMC'06 Proceedings of the 7th international conference on Membrane Computing
WMC'06 Proceedings of the 7th international conference on Membrane Computing
An optimization algorithm inspired by membrane computing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
WMC'05 Proceedings of the 6th international conference on Membrane Computing
Radar emitter signal recognition based on feature selection algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Harmonic decomposition of audio signals with matching pursuit
IEEE Transactions on Signal Processing
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
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To decrease the computational complexity and improve the search capability of quantum-inspired evolutionary algorithm based on P systems (QEPS), a real-observation QEPS (RQEPS) was proposed. RQEPS is a hybrid algorithm combining the framework and evolution rules of P systems with active membranes and real-observation quantum-inspired evolutionary algorithm (QEA). The RQEPS involves a dynamic structure including membrane fusion and division. The membrane fusion is helpful to enhance the information communication among individuals and the membrane division is beneficial to reduce the computational complexity. An NP-complete problem, the time-frequency atom decomposition of noised radar emitter signals, is employed to test the effectiveness and practical capabilities of the RQEPS. The experimental results show that RQEPS is superior to QEPS, the greedy algorithm and binary-observation QEA in terms of search capability and computational complexity.