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A compact genetic algorithm for the network coding based resource minimization problem

APPLIED INTELLIGENCE 2012年第4期36卷 809-823页

作者： Xing, Huanlai Qu, Rong Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning ASAP Gr Nottingham NG8 1BB England

In network coding based data transmission, intermediate nodes in the network are allowed to perform mathematical operations to recombine (code) data packets received from different incoming links. Such coding operations incur additional computational overhead and consume public resources such as buffering and computational resource within the network. Therefore, the amount of coding operations is expected to be minimized so that more public resources are left for other network applications. In this paper, we investigate the newly emerged problem of minimizing the amount of coding operations required in network coding based multicast. To this end, we develop the first elitism-based compact genetic algorithm (cGA) to the problem concerned, with three extensions to improve the algorithm performance. First, we make use of an all-one vector to guide the probability vector (PV) in cGA towards feasible individuals. Second, we embed a PV restart scheme into the cGA where the PV is reset to a previously recorded value when no improvement can be obtained within a given number of consecutive generations. Third, we design a problem-specific local search operator that improves each feasible solution obtained by the cGA. Experimental results demonstrate that all the adopted improvement schemes contribute to an enhanced performance of our cGA. In addition, the proposed cGA is superior to some existing evolutionary algorithms in terms of both exploration and exploitation simultaneously in reduced computational time.

关键词： Compact genetic algorithm estimation of distribution algorithm Multicast Network coding

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Improved Mutual Information Maximizing Input Clustering algorithm for Permutation Flow Shop Scheduling Problem

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SENSOR LETTERS 2012年第8期10卷 1862-1866页

作者： Gao, Huimin Wang, Xiaoping Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Peoples R China

Improved Mutual Information Maximizing Input Clustering algorithm is a kind of discrete estimation of distribution algorithm, which is convenient to solve permutation flow shop scheduling problem. In this paper, the encoding mode and probability model are improved, new individual strategy is proposed, greedy algorithm is introduced at the initial phase of the probability matrix, and crossover operator, mutation operator, insert operator and swap operator are adopted during the process of evolution, dynamic adjusted method is employed to determine the population size. These improvements gurantee the population diversity even in small population. Experiment results show that the improved Mutual Information Maximizing Input Clustering algorithm is effective and stable.

关键词： Mutual Information Maximizing Input Clustering algorithm Permutation Flow Shop Scheduling Problem estimation of distribution algorithm Probability Matrix

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Evolving NK-complexity for Evolutionary Solvers 12

Evolving NK-complexity for Evolutionary Solvers

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Santana, Roberto Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU P Manuel Lardizabal 1 San Sebastian 20018 Spain

ISBN: (纸本)9781450311786

In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measure, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

关键词： estimation of distribution algorithm NK-model complexity analysis networks

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Maximizing the Number of Polychronous Groups in Spiking Networks 12

Maximizing the Number of Polychronous Groups in Spiking Netw...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Santana, Roberto Bielza, Concha Larranaga, Pedro Univ Basque Country UPV EHU P Manuel de Lardizabal 1 Donostia San Sebastian 20018 Spain Univ Politecn Madrid DIA Madrid Spain

ISBN: (纸本)9781450311786

In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.

关键词： polychronization spiking neural network estimation of distribution algorithm

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An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2007年第12期58卷 1574-1585页

作者： Aickelin, U. Burke, E. K. Li, J. Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work ( where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

关键词： nurse rostering estimation of distribution algorithm local search ant colony optimization

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A Hybrid Adaptive Evolutionary algorithm in the Domination-based and Decomposition-based Frameworks of Multi-objective Optimization

A Hybrid Adaptive Evolutionary Algorithm in the Domination-b...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism that synthesizes a genetic algorithm, differential evolution and estimation of distribution algorithm. The adaptive mechanism takes into account the ratio of the number of promising solutions generated from each optimizer in an early stage of evolutions so as to determine the proportion of the number of solutions to be produced by each optimizer in the next generation. Furthermore, the adaptive algorithm is also hybridized with the evolutionary gradient search to further enhance its search ability. The proposed hybrid adaptive algorithm is developed in the domination-based and decomposition-based multi-objective frameworks. An extensive experimental study is carried out to test the performances of the proposed algorithms in 38 state-of-the-art benchmark test instances.

关键词： Decomposition differential evolution domination genetic algorithm estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization

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Genetic & Evolutionary Biometrics: Hybrid Feature Selection and Weighting for a Multi-Modal Biometric System

Genetic & Evolutionary Biometrics: Hybrid Feature Selection ...

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IEEE SoutheastCon

作者： Alford, Aniesha Steed, Crystal Jeffrey, Marcus Sweet, Donovan Shelton, Joseph Small, Lasanio Leflore, Derrick Dozier, Gerry Bryant, Kelvin Kelly, John C. Abegaz, Tamirat Ricanek, Karl N Carolina Agr & Tech State Univ Ctr Adv Studies Ident Sci Greensboro NC 27411 USA Clemson Univ Comp Sci Clemson SC USA North Carolina A&T State Univ Elect & Comp Engn Greensboro NC USA Univ N Carolina Ctr Adv Studies Ident Sci Wilmington NC USA

ISBN: (纸本)9781467313759

The Genetic & Evolutionary Computation (GEC) research community is seeing the emergence of a new and exciting subarea, referred to as Genetic & Evolutionary Biometrics (GEB), as GECs are increasingly being applied to a variety of biometric problems. In this paper, we present successful GEB techniques for multi-biometric fusion and multi-biometric feature selection and weighting. The first technique, known as GEF (Genetic & Evolutionary Fusion), seeks to optimize weights for score-level fusion. The second technique is known as GEFeWSML (Genetic & Evolutionary Feature Weighting and Selection-Machine Learning). The goal of GEFeWSML is to evolve feature masks (FMs) that achieve high recognition accuracy, use a low percentage of features, and generalize well to unseen subjects. GEFeWSML differs from the other GEB techniques for feature selection and weighting in that it incorporates cross validation in an effort to evolve FMs that generalize well to unseen subjects.

关键词： Biometrics Cross Validation estimation of distribution algorithm Feature Selection Feature Weighting Genetic & Evolutionary Computation Local Binary Pattern

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Genetic & Evolutionary Biometrics: Feature Extraction from a Machine Learning Perspective

Genetic & Evolutionary Biometrics: Feature Extraction from a...

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IEEE SoutheastCon

作者： Shelton, Joseph Alford, Aniesha Small, Lasanio Leflore, Derrick Williams, Jared Adams, Joshua Dozier, Gerry Bryant, Kelvin Abegaz, Tamirat Ricanek, Karl NC A&T Ctr Adv Studies Ident Sci Greensboro NC USA Clemson Univ Comp Sci Clemson SC USA Univ N Carolina Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9781467313759

Genetic & Evolutionary Biometrics (GEB) is a newly emerging area of study devoted to the design, analysis, and application of genetic and evolutionary computing to the field of biometrics. In this paper, we present a GEB application called GEFEML (Genetic and Evolutionary Feature Extraction Machine Learning). GEFEML incorporates a machine learning technique, referred to as cross validation, in an effort to evolve a population of local binary pattern feature extractors (FEs) that generalize well to unseen subjects. GEFEML was trained on a dataset taken from the FRGC database and generalized well on two test sets of unseen subjects taken from the FRGC and MORPH databases. GEFEML evolved FEs that used fewer patches, had comparable accuracy, and were 54% less expensive in terms of computational complexity.

关键词： Biometrics Cross Validation estimation of distribution algorithm Feature Extraction Genetic & Evolutionary Computation Local Binary Pattern

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Asynchronous Strategy of Parallel Hybrid Approach of GA and EDA for Function Optimization

Asynchronous Strategy of Parallel Hybrid Approach of GA and ...

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3rd International Conference on Networking and Computing (ICNC)

作者： Said, Said Mohamed Nakamura, Morikazu Univ Ryukyus Dept Informat Engn Nishihara Okinawa 9030213 Japan

ISBN: (纸本)9780769548937;9781467346245

This paper adapts parallel master-slave estimation of distribution and genetic algorithms (GAs and EDAs) hybridization. The master selects portions of the search space, and slaves perform, in parallel and independently, a GA that solves the problem on the assigned portion of the search space. The master's work is to progressively narrow the areas explored by the slave's GAs, using parallel dynamic K-means clustering to determine the basins of attraction of the search space. Coordination of activities between master and slaves is done in an asynchronous way (i.e. no waiting is entertained among the processes). The proposed asynchronous model has managed to reduce computation time while maintaining the quality of solutions.

关键词： Hybrid estimation of distribution algorithm Genetic algorithms Synchronous Asynchronous Parallel processing Master-Slave K-means clustering

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Global Multiobjective Optimization via estimation of distribution algorithm with Biased Initialization and Crossover 07

Global Multiobjective Optimization via Estimation of Distrib...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Zhou, Aiming Zhang, Qingfu Jin, Yaochu Sendhoff, Bernhard Tsang, Edward Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England Honda Res Inst Europe D-63073 Offenbach Germany

ISBN: (纸本)9781595936974

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary agorithms. In this paper, two operators, biased initialization and biased crossover, axe proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population;biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.

关键词： global optimization multiobjective optimization estimation of distribution algorithm biased initialization biased crossover

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