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Evolutionary Computation (CEC), 2010 IEEE Congress on

Date 18-23 July 2010

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Displaying Results 1 - 25 of 628
  • Index [of articles]

    Page(s): 1 - 48
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    Page(s): 1 - 41
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  • Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (873 KB) |  | HTML iconHTML  

    As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), a new approach named Genetic Network Programming (GNP) has been proposed in the evolutionary computation field. GNP uses multiple reusable nodes to construct directed-graph structures to represent its solutions. Recently, many research has clarified that GNP can work well in data mining area. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to solve traffic prediction problems using class association rule mining. In GNP-EDAs, a probabilistic model is constructed by estimating the probability distribution from the selected elite individuals of the previous generation to replace the conventional genetic operators, such as crossover and mutation. The probabilistic model is capable of enhancing the evolution to achieve the ultimate objective. In this paper, two methods are proposed based on extracting the probabilistic information on the node connections and node transitions of GNP-EDAs to construct the probabilistic model. A comparative study of the proposed paradigm and the conventional GNP is made to solve the traffic prediction problems using class association rule mining. The simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increases. And the classification accuracy of the proposed method shows good results in traffic prediction systems. View full abstract»

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  • Generating a novel sort algorithm using Reinforcement Programming

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    Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Results establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. RP has also been used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder. View full abstract»

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  • Design of digital FIR filters using differential evolution algorithm based on reserved genes

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    Filtering has been an enabling technology and has found ever-increasing applications. There are two main classes of digital filters: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. FIR filter can be guaranteed to have linear phase and are always stable filters, so FIR filters is widely applicable. The differential evolution (DE) algorithm, which has been proposed particularly for numeric optimization problems, is a population-based algorithm like the genetic algorithms. In this work, the new DE algorithm based on reserved genes has been applied to the design of digital finite impulse response filters. In this new algorithm, the new vectors can be produced by the combination of genes of the selected chromosomes. These new vectors as the new individuals are evolved with other individuals in the population. It can increase the diversity of population and the algorithm is effective to avoid the local optimal solution. It can get more accurate solution. Its performance has been compared to other method. Examples are illustrated to demonstrate the effectiveness of the proposed design method. View full abstract»

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  • Optimal Reactive Power Planning for load margin improvement using Multi Agent Immune EP

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    System's loadability is very much depends on the reactive power support in the network. Lack of reactive power support may results in a serious problem to occur i.e. voltage collapse. This paper presents a method to increase the system's loadability and hence the voltage stability margin of a system by utilizing the Optimal Reactive Power Planning (ORPP). A newly developed optimization technique; namely Multiagent Immune Evolutionary Programming (MAIEP) is introduced to obtain the optimal solution to the problem. The concept of MAIEP is developed based on the combination of Multiagent System, Artificial Immune System and Evolutionary Programming. The proposed MAIEP based ORPP was tested on the IEEE-26 reliability test system in order to realize its performance. The results obtained from the proposed ORPP using MAIEP has successfully improved the load margin and at the same time the total system losses and cost of generation were reduced. View full abstract»

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  • Multiple ODs routing algorithm for traffic systems using GA

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    The multiple origins multiple destinations routing (MOMDR) problem becomes extremely complicated when considering the traffic volumes on road sections. When solving this kind of problem, only heuristic algorithms have practical values because it is a typical NP-Hard problem. This paper applies Genetic Algorithm (GA) to enhance Sorting-Randomizing-Adjusting-Updating (SRAU) algorithm. The previous paper shows that different processing orders of the origin-destinations (ODs) result in different solutions with different performances. Therefore, an algorithm for finding the best processing order of ODs can optimize SRAU algorithm. In this paper, the processing order of ODs is transformed into a gene/chromosome of the individual of GA; then, the best gene can be found by evolution; finally, the best gene is transformed back to find the optimal solution of the problem. Sufficient simulations show that the proposed algorithm is more efficient than original SRAU algorithm. Comparisons also show that the proposed algorithm has higher performance and faster convergence speed than RAND algorithm which uses the random policy to find the proper processing order of ODs. Moreover, the consideration of the traffic volumes on the road sections enables the proposed algorithm to be applied to real traffic systems. View full abstract»

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  • Improving the performance of particle swarms through dimension reductions — A case study with locust swarms

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (685 KB) |  | HTML iconHTML  

    A key challenge for many heuristic search techniques is scalability - techniques that work well on low-dimension problems may perform poorly on high-dimension problems. To the extent that some problems/problem domains are separable, this can lead to a benefit for search techniques that can exploit separability. The standard algorithm for particle swarm optimization does not provide opportunities to exploit separable problems. However, the design of locust swarms involves two phases (scouts and swarms), and “dimension reductions” can be easily implemented during the scouts phase. This ability to exploit separability in locust swarms leads to large performance improvements on separable problems. More interestingly, dimension reductions can also lead to significant performance improvements on non-separable problems. Results on the Black-Box Optimization Benchmarking (BBOB) problems show how dimension reductions can help locust swarms perform better than standard particle swarms - especially on high-dimension problems. View full abstract»

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  • Principles of protein processing for a self-organising associative memory

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    The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Most parallel hardware architectures tend to combine Von Neumann style processors to make a multi-processor environment and computation is based on Arithmetic and Logic Units (ALU). This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations. The architecture proposed here is targeted towards robust artificial intelligence applications. View full abstract»

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  • Generalized rule extraction and traffic prediction in the optimal route search

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1022 KB) |  | HTML iconHTML  

    Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with MBFP(Multi-Branch and Full-Pathes) processing mechanism has been introduced in order to find time related sequential rules more efficiently. GNP represents solutions as directed graph structures, thus has compact structure and partially observable Markov decision process. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction and its usage. The generalized algorithm which can find the important time related association rules has been proposed and experimental results are presented considering how to use the rules to predict the future traffic volume and also how to use the traffic prediction in the optimal search problem. View full abstract»

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  • Differential Evolution enhanced by neighborhood search

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    This paper presents a novel Differential Evolution (DE) algorithm, called DE enhanced by neighborhood search (DENS), which differs from pervious works of utilizing the neighborhood search in DE, such as DE with neighborhood search (NSDE) and self-adaptive DE with neighborhood search (SaNSDE). In DENS, we focus on searching the neighbors of individuals, while the latter two algorithms (NSDE and SaNSDE) work on the adaption of the control parameters F and CR. The proposed algorithm consists of two following main steps. First, for each individual, we create two trial individuals by local and global neighborhood search strategies. Second, we select the fittest one among the current individual and the two created trial individuals as a new current individual. Experimental studies on a comprehensive set of benchmark functions show that DENS achieves better results for a majority of test cases, when comparing with some other similar evolutionary algorithms. View full abstract»

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  • Bidirectional matrix-based algorithm for 4-qubit reversible logic circuits synthesis

    Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    Quantum reversible logic circuits synthesis is one of the key technologies to construct quantum computer. The algebraic model for quantum information processing is a unitary matrix operator. Matrix can better reflect the quantum state evolution and the properties of quantum computation. Bidirectional matrix-based algorithm for quantum reversible logic circuits synthesis is proposed in this paper. The matrix representation of quantum reversible circuit and the circuit transformation rules of adjacent matrix are employed to construct any quantum reversible circuit in this paper. Compared with, the computational complexity of our algorithm has been decreased exponentially and the speed has been increased by about 105 times. In addition, the types of the quantum reversible circuits synthesized by our algorithm are extended from only even permutations in to even and odd ones. we have synthesized 13!=6227020800 quantum reversible circuits, which can't be done by other algorithms. View full abstract»

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  • Incorporation of imprecise goal vectors into evolutionary multi-objective optimization

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    Preference-based techniques in multi-objective evolutionary algorithms (MOEA) are gaining importance. This paper presents a method of representing, eliciting and integrating decision making preference expressed as a set of imprecise goal vectors into a MOEA with steady-state replacement. The specification of a precise goal vector without extensive knowledge of problem behavior often leads to undesirable results. The approach proposed in this paper facilitates the linguistic specification of goal vectors relative to extreme, non-dominated solutions (i.e. the goal is specified as ”Very Small”, ”Small”, ”Medium”, ”Large”, and ”Very Large”) with three degrees of imprecision as desired by the decision maker. The degree of imprecision corresponds to the density of solutions desired within the target subset. Empirical investigations of the proposed method yield promising results. View full abstract»

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  • Hostile area or facility monitoring with an optimal wireless sensor network deployment

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    This study uses an enhanced non-dominated sorting genetic algorithm (enhanced MOGA) to generate the optimal wireless sensor network deployment for monitoring a hostile perimeter area or a critical facility. The sensors are deployed around the area to sense the activities in the area or placed outside the critical facility for sensing the movements of incoming and outgoing of personnel worked in the facility. The distributed sensors are capable of sensing and linking with each other in order to communicate the gathered data via a sensor to a nearby high energy communication node (HECN). The HECN served as a transmission relay to deliver gathered data from the ground to a high-altitude unmanned aerial vehicle (UAV). Two scenarios are implemented by using the enhanced MOGA to achieve the sensor deployment by minimizing the number of sensors and maximizing the coverage. Simulation results will show the Pareto-optimal front, sensor deployment and communication routes between sensors and the HECN. View full abstract»

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  • A genetic algorithm for generating multiple paths on mesh maps

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    Path generation is an optimization problem mainly performed on grid square maps that combines generation of paths with minimization of their cost. Several methods that belong to the class of exhaustive searches are available; however, these methods are only able to obtain a single path as a solution for each iteration of the search. Hence, this paper proposes a new method using genetic algorithms for this problem with the goal of simultaneously searching for multiple candidate paths. View full abstract»

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  • An efficient quantum secret sharing scheme based on orthogonal product states

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (629 KB) |  | HTML iconHTML  

    We propose an efficient quantum secret sharing scheme with orthogonal product states in the 3×3 Hilbert space. Different from Hsu and Li's scheme [Phys. Rev. A 71, 022321 (2005)], this scheme utilizes a novel distribution strategy which sends the qutrits of basis states and superpositions to two separated observers respectively. The theoretical analysis shows that the intrinsic efficiency for qutrits in this scheme approaches 100% and the total efficiency of this scheme is higher than that of the aforementioned scheme. Furthermore, the security and some possible eavesdropping strategies are also examined in this paper. View full abstract»

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  • Guiding the evolution of Genetic Network Programming with reinforcement learning

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (848 KB) |  | HTML iconHTML  

    Genetic Network Programming (GNP) is one of the evolutionary algorithms. It adopts a directed graph structure to represent a solution to a given problem. Agents judge situations and execute actions sequentially following the node transitions in the graph. On one hand, GNP possesses an advantage of node reusability, which makes it possible to realize a compact graph structure that represents a solution. On the other hand, the compact structure suggests that any connection might play a significant role in the solution, i.e., a slight change to the connections could tremendously influence the performance of the agents for the given task. The conventional GNP, however, lacks an effective way to evaluate and to take advantage of the connections. This paper thus proposes a reinforcement learning approach to learn GNP's subgraphs that contain a relatively small number of connections, and further proposes a partial reconstruction approach to modify the solution with the obtained subgraphs. These two approaches are combined together to form a new evolutionary learning model named GNP with Evolution-oriented Reinforcement Learning (GNP-ERL). Some experiments are conducted on the Tileworld testbed to verify the effectiveness of GNP-ERL, and the simulation results demonstrate that it outperforms the conventional GNP in both training and testing phases. View full abstract»

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  • Visualization of hidden structures in corporate failure prediction using opposite pheromone per node model

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (704 KB) |  | HTML iconHTML  

    The oppositional and antipodal forms of forces, entities and quantities have been envisaged in the context of practical and applied field of engineering and management science in order to create a more complete picture of reality. The interplay between entities and opposite entities is apparently fundamental for maintaining universal balance. A large number of problems in engineering and science cannot be approached with conventional schemes and are generally handled with intelligent techniques such as evolutionary, neural, reinforcing and swarm-based methods. Visualization of unforeseen financial events is one of those applications, where failure of particular corporate firm can be forecasted based on the combination of several indicators. The hidden artifacts of corporate financial events could also be evaluated with the help of ant-based behavior of pheromone deposition. The learning in the pheromone deposition is subjected to oppositional forces leading towards the equilibrium of the corporate of interest. The motivation of this paper is to initiate the model to analyze and to represent the financial practices of typical clusters, which may cause failure of that firm in near future. In this paper we propose to use opposition based learning and Soft Bergman Based Clustering to implement the proposed model. Brief comparison of results is presented at the end of the proposal. View full abstract»

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  • An adaptive niching EDA based on clustering analysis

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1227 KB) |  | HTML iconHTML  

    Estimation of Distribution Algorithms (EDAs) still suffer from the drawback of premature convergence for solving the optimization problems with irregular and complex multimodal landscapes. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first is the continuous EDA based on single Gaussian probabilistic model to solve two benchmark functional multimodal optimization problems. The second is a real complicated discrete EDA optimization problem, the protein 3-D HP model based on k-order Markov probabilistic model. The experiment studies demonstrate that the proposed adaptive niching EDA is an efficient method. View full abstract»

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  • An effective GSA based memetic algorithm for permutation flow shop scheduling

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (177 KB) |  | HTML iconHTML  

    The permutation flow shop problem (PFSSP) is a well-known difficult combinatorial optimization problem. In this paper, we present a new hybrid optimization algorithm named SIGSA to solve the PFSSP. This algorithm is composed by the LRV rule, SA-based local search and IIS-based local search. First, to make GSA suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in GSA to the discrete job permutation. Second, to enhance the searching capability, the SA-based local search is designed to help the algorithm to escape from local minimum. Then, the IIS-based local search is used for enhancing the individuals in GSA with a certain probability. Additionally, Comparison with other results in the literature shows that the SIGSA is an efficient and effective approach for the PFSSP. View full abstract»

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  • Computational discovery of regulatory DNA motifs using evolutionary computation

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    Computational discovery of DNA motifs is one of the major challenges in bioinformatics, which helps in understanding the mechanism of gene regulation. It has been reported that computational approaches have good potential for problem solving in terms of cost and time saving. Based on our previous studies, this paper aims to develop an evolutionary computation scheme to provide an alternative approach for motif discovery. To work on the framework of our previously developed GAPK, a small sized collection of k-mers is extracted and utilized as “prior knowledge” in algorithm development. Our technical contributions in this paper mainly include a novel fitness function carrying information on conservation and rareness of DNA motifs, and a path to access GAPK-like solutions using seed concept and filtering techniques. The proposed algorithm in this paper has been evaluated by using eight benchmarked datasets, with comparisons to well-known tools such as MEME, MDScan, AlignACE and two GA-based techniques. Results show that our proposed method favorably outperforms other algorithms for these testing datasets. View full abstract»

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  • Metamorphic systems: A new model for adaptive system design

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    The evolvable hardware field has been an area of research interest since the early 1990s. However, the number of significant accomplishments has noticeably diminished in recent years. In this paper we discuss several reasons why this is so. We then introduce a new model called metamorphic systems that builds upon evolvable hardware principles placing more emphasis on adapting system behavior rather than evolving desired system behavior. Our metamorphic system approach is better suited to non-electronic systems. Two example metamorphic systems are presented. View full abstract»

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  • Co-evolutionary search path planning under constrained information-sharing for a cooperative unmanned aerial vehicle team

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    Mobile cooperative sensor networks are increasingly used for surveillance and reconnaissance tasks to support domain picture compilation. However, efficient distributed information gathering such as target search by a team of autonomous unmanned aerial vehicles (UAVs) remains very challenging in constrained environment. In this paper, we propose a new approach to learn resource-bounded multi-agent coordination for a multi-UAV target search problem subject to stringent communication bandwidth constraints in a dynamic uncertain environment. It relies on a new information-theoretic co-evolutionary algorithm to solve cooperative search path planning over receding horizons, providing agents with mutually adaptive and self-organizing behavior. The anytime coordination algorithm is coupled to a divergence-based information-sharing policy to exchange high-value world-state information under limited communication bandwidth. Computational results show the value of the proposed approach in comparison to a well-known reported technique. View full abstract»

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  • Evolutionary scheduling with rescheduling option for sudden machine breakdowns

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (110 KB) |  | HTML iconHTML  

    The job scheduling problem (JSP) is considered as one of the complex combinatorial optimization problems. In this paper, we have developed a hybrid Genetic Algorithm (HGA), which improves the performance of GAs when solving JSPs. We have also modified the developed algorithm to study JSPs under the machine unavailability condition. We have considered two types of machine unavailability. Firstly, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We have shown that the revised schedule is mostly able to recover if the disruptions occur during the early stages of a schedule. View full abstract»

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  • Quantum-inspired immune clonal clustering algorithm based on watershed

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    Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM). View full abstract»

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