Showing papers by "Gexiang Zhang published in 2011"
TL;DR: A unified framework and a comprehensive survey of recent work in quantum-inspired evolutionary algorithms is provided and conclusions are drawn about some of the most promising future research developments in this rapidly growing field.
Abstract: Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.
225 citations
01 Jan 2011
TL;DR: The FSN P system is proposed, which is especially suitable to model fuzzy production rules in a rule-based system, and content of neuron is fuzzy number instead of natural number (the number of spikes) in SN P systems.
Abstract: In order to extend capability of spiking neural P systems (SN P systems) to represent fuzzy knowledge and further to process fuzzy information, we propose an extended spiking neural P system in this paper, called fuzzy spiking neural P system (FSN P system). In the FSN P system, two types of neurons (fuzzy proposition neuron and fuzzy rule neuron), certain factor and new spiking rule are considered, and content of neuron is fuzzy number instead of natural number (the number of spikes) in SN P systems. Due to graphical nature and advantages of SN P systems, the FSN P system is especially suitable to model fuzzy production rules in a rule-based system. An example is used to illustrate fuzzy reasoning process based on the FSN P system. Due to distributed and parallel computing and dynamical
32 citations
Journal Article•
TL;DR: The hierarchical structure of cell-like P systems is used to organize the objects consisting of real-valued strings and the rules which are composed of mutation, crossover and selection operations in elementary membranes, a local search in the skin membrane and transformation/communicationlike rules in P systems.
Abstract: This paper presents a novel membrane algorithm, called DEPS, for numerical optimization. DEPS is an appropriate combination of a differential evolution algorithm, a local search and P systems. In this algorithm, the hierarchical structure of cell-like P systems is used to organize the objects consisting of real-valued strings and the rules which are composed of mutation, crossover and selection operations in elementary membranes, a local search in the skin membrane and transformation/communicationlike rules in P systems. The effectiveness of the algorithm is tested on extensive numerical optimization experiments. In what follows DEPS is applied to solve a real-world problem, time-frequency atom decomposition. Experimental results show that DEPS performs better than its counterpart differential evolution algorithm.
31 citations
23 Aug 2011
TL;DR: Experiments conducted on P-lingua simulator show that the presented design approach is feasible and effective to automatically evolve a membrane system for solving some specific tasks and that a quantum-inspired evolutionary algorithm is more appropriate than a genetic algorithm for designing a membranes system.
Abstract: The programmability of membrane systems is an ongoing and challenging issue. This paper focuses on the automatic design of a simple membrane system for fulfilling a specific task by using a quantum-inspired evolutionary algorithm and the P-lingua simulator. The design consists of the pre-defined membrane structure and initial objects, a set of possible evolution rules, the coding technique of membrane systems, evolutionary operators and a fitness function for evaluating different membrane systems. Experiments conducted on P-lingua simulator show that the presented design approach is feasible and effective to automatically evolve a membrane system for solving some specific tasks. The results also show that a quantum-inspired evolutionary algorithm is more appropriate than a genetic algorithm, recently reported in the literature, for designing a membrane system.
15 citations
23 Dec 2011
TL;DR: This paper proposes a Comprehensive Learning Quantum-Inspired Evolutionary Algorithm (CLQEA) by introducing the philosophy of comprehensive learning into quantum-inspired evolutionary algorithms and showing that CLQEA outperforms several QIEAs recently reported in the literature.
Abstract: This paper proposes a Comprehensive Learning Quantum-Inspired Evolutionary Algorithm (CLQEA) by introducing the philosophy of comprehensive learning into quantum-inspired evolutionary algorithms. In CLQEA, each individual in a population learns not only from its own best historical solution searched, but also from the best solutions that other individuals found. This idea is very helpful to enhance population diversity through applying a group of elite individuals to perform Q-gates to produce offspring. Extensive experiments carried out on knapsack problems with various items show that CLQEA outperforms several QIEAs recently reported in the literature.
7 citations
25 Mar 2011
TL;DR: The proposed PSO-CLO strategy can be used for delay sensitive, bandwidth intensive and loss-tolerant wireless multimedia transmissions that have an ever demanding need for better Quality of Service.
Abstract: Cross Layer Optimization (CLO) strategies are currently being incorporated in network operating system for efficient utilization of resources to enable effective information management. In wireless adhoc networks real time optimizations need to be performed and hence CLO strategies that have faster response time are required. In this paper we propose a Cross Layer Optimization strategy that uses a variant of the Particle Swarm Optimization (PSO) for real time cross layer design of the network. The variant of the PSO used in this research work uses digital pheromones for improved performance. The proposed PSO-CLO strategy can be used for delay sensitive, bandwidth intensive and loss-tolerant wireless multimedia transmissions that have an ever demanding need for better Quality of Service. Our experimental results show that the proposed PSO-CLO strategy has significantly faster response time in comparison with the classical CLO solutions.
2 citations
27 Sep 2011
TL;DR: mEDA uses a novel sampling method, called centro-individual sampling, and a fuzzy c-means clustering technique to improve its performance, and extensive experiments show that mEDA outperforms HPBILc, C EGDA, CEGNABGe and NichingEDA, reported in the literature, in terms of the quality of solutions.
Abstract: Estimation of distribution algorithms (EDAs) is a class of probabilistic model-building evolutionary algorithms, which is characterized by learning and sampling the probability distribution of the selected individuals. This paper proposes a modified estimation of distribution algorithm (mEDA) for numeric optimization. mEDA uses a novel sampling method, called centro-individual sampling, and a fuzzy c-means clustering technique to improve its performance. Extensive experiments conducted on a set of benchmark functions show that mEDA outperforms HPBILc, CEGDA, CEGNABGe and NichingEDA, reported in the literature, in terms of the quality of solutions.
01 Jan 2011
TL;DR: This paper illustrates a situation in which the FSV results do not agree with visual expectation: data sets consist of a high transient event and relative low pre-transients and post-transient regions, and it is shown that the modifled FSV method could avoid the error and has no in∞uence on the comparing of other data sets.
Abstract: As the Feature Selective Validation (FSV) technique is becoming a dominant quan- titative validation method of computational electromagnetic simulation results Some studies have been undertaken, and subsequent enhancements proposed, to improve its performance This paper illustrates a situation in which the FSV results do not agree with visual expectation: data sets consist of a high transient event and relative low pre-transient and post-transient regions The reasons for this discrepancy are discussed in detail in this paper and shown to be related to the Ofiset Difierence Measure (ODM) which is then modifled in order to avoid this apparent error without any pre-processing to original data sets It is flnally shown that the modifled FSV method could avoid the error and has no in∞uence on the comparing of other data sets