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22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK)

作者： Stroie, Oana Apostol, Elena-Simona Truica, Ciprian-Octavian Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9783030590659;9783030590642

A large number of real-world optimization and search problems are too computationally intensive to be solved due to their large state space. Therefore, a mechanism for generating approximate solutions must be adopted. genetic algorithms, a subclass of Evolutionary algorithms, represent one of the widely used methods of finding and approximating useful solutions to hard problems. Due to their population-based logic and iterative behaviour, Evolutionary algorithms are very well suited for parallelization and distribution. Several distributed models have been proposed to meet the challenges of implementing parallel Evolutionary algorithms. Among them, the MapReduce paradigm proved to be a proper abstraction of mapping the evolutionary process. In this paper, we propose a generic framework, i.e., DHE2 (distributed Hybrid Evolution Engine), that implements distributed Evolutionary algorithms on top of the MapReduce open-source implementation in Apache Hadoop. Within DHE2, we propose and implement two distributed hybrid evolution models, i.e., the MasterSlavelslands and Micro-Macrolslands models, alongside a real-world application that avoids the local optimum for clustering in an efficient and performant way. The experiments for the proposed application are used to demonstrate DHE2 increased performance.

关键词： distributed Hybrid Evolution Engine Hybrid evolution models distributed genetic algorithms MapReduce

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Effective distributed genetic algorithms for Optimizing Social Utility

Effective Distributed Genetic Algorithms for Optimizing Soci...

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13th IEEE International Conference on Commerce and Enterprise Computing (CEC)

作者： Mizutani, Nobuyasu Fujita, Katsuhide Ito, Takayuki Nagoya Inst Technol Comp Sci & Engn Nagoya Aichi Japan Nagoya Inst Technol Sch Technobusiness Adm Nagoya Aichi Japan

ISBN: (纸本)9780769545356

Most real-world negotiation involves multiple interdependent issues that makes an agent's utility function nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability, which can produce excessively high failure rate when there are many issues due to computational intractability. One reasonable approach to reducing computational cost while maintaining quality outcomes is to decompose the utility space into several largely independent sub spaces. In this paper, we propose a new method for decomposing a utility space based on the interdependency of issues and employing the genetic algorithms in each issue-group. In addition, our experimental results demonstrate that our method can find higher quality solutions than existing works. They also show that our method is highly effective for reducing the execution time.

关键词： Multi-issue Negotiation distributed genetic algorithms Nonlinear Utility Function

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A Parallel distributed genetic Algorithm for the Prize Collecting Steiner Tree Problem

A Parallel Distributed Genetic Algorithm for the Prize Colle...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Rojas, Francisco Meza, Federico Univ Talca Sch Engn Camino Los Niches Km 1 S-N Curico Chile

ISBN: (纸本)9781467397957

Combinatorial optimization problems are commonly tackled through the use of metaheuristics aimed to improve the way the search space is explored (diversification) and how promising areas are exploited (intensification), in order to obtain good-quality solutions. We present a distributed genetic algorithm to solve the Prize Collecting Steiner Tree Problem, a classical combinatorial optimization problem. The proposed algorithm improves the quality of the solutions by asynchronously combining distributed populations that evolve in parallel, starting from initial heterogeneous configurations. To show the effectiveness of this approach an empirical study that considers both execution time and solution quality is presented. Results show that better solutions are reached when initial populations combine configurations with high and low fitness.

关键词： distributed genetic algorithms PCST Prize Collecting Steiner Tree Problem Parallel Metaheuristics

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distributed GAs with Case-Based Initial Populations for Real-Time Solution of Combinatorial Problems

Distributed GAs with Case-Based Initial Populations for Real...

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2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

作者： Kawabe, Takashi Suzuki, Masaki Matsumaru, Taro Yamamoto, Yukiko Tsuruta, Setsuo Sakurai, Yoshitaka Knauf, Rainer Tokyo Denki Univ Sch Informat Environm Inzai Japan Meiji Univ Sch Interdisciplinary Math Sci Nakano Japan Ilmenau Univ Technol Fac Comp Sci & Automat Ilmenau Germany

ISBN: (纸本)9781479944941

Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as genetic algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, we introduce distributed GAs, which compute their initial population in a case-based manner and compose their upcoming generations by the particular GAs, which exchange their solutions and make their individual decisions, when composing a next generation based on the fitness of the candidates and diversity issues.

关键词： Travelling Salesman Problem Case Based Reasoning distributed Computing distributed genetic algorithms Parallel Processing

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DGA: distributed genetic Algorithm Based Relay Assignment in Cooperative Communication

DGA: Distributed Genetic Algorithm Based Relay Assignment in...

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9th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： An, Ruijian Ji, Yusheng Grad Univ Adv Studies Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (纸本)9781467324809

A crucial challenge in the implementation of cooperative protocol is how to assign relay nodes properly. In this paper, we address the relay assignment issue in the wireless networks where the source nodes, destination nodes and relay nodes are randomly distributed in a large area. Without any kind of central controller, we try to solve the relay assignment problem in a distributed manner with the local information. Based on this network model, we developed a distributed genetic Algorithm (DGA) based scheme to pursue a better system capacity compared with other distributed algorithms. We also presented a brief discussion and analysis on the DGA algorithm. At last, extensive simulations show that the DGA outperforms other distributed algorithms with obvious improvement of the system capacity and converges fast.

关键词： Relay assignment Cooperative communication distributed genetic algorithms

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Autonomous distributed GA for Solving Real-time Combinatorial Problems

Autonomous Distributed GA for Solving Real-time Combinatoria...

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9th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Kobayashi, Yuuta Suzuki, Masaki Tsuruta, Setsuo Sakurai, Yoshitaka Tokyo Denki Univ Sch Informat Environm Inzai Japan Meiji Univ Sch Interdisciplinary Math Sci Tokyo 101 Japan

ISBN: (纸本)9781479932115

Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as genetic algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, this inefficiency is shown by simulation using TSP library as the example of optimal route scheduling. Then, an autonomous distributed GA to cope with this inefficiency through exchanging information about individuals (to calculate fitness /divergence /situation) among autonomous CPUs is proposed in solving real-time combinatorial problems. Using TSP library again, its effectiveness is shown by simulation experiments.

关键词： Ditributed Computing distributed genetic algorithms Parallel Processing Traveling Salesman Problems (TSP)

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Metamodel-assisted distributed genetic algorithms applied to structural shape optimization problems

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ENGINEERING OPTIMIZATION 2007年第7期39卷 757-772页

作者： Annicchiarico, W. Cent Univ Venezuela Fac Ingn Inst Mat & Modelos Estruct Caracas 1050A Venezuela

This article is concerned with the development of a general optimization tool based on distributed real genetic algorithms ( DRGAs) assisted by metamodel evaluation and applied to structural shape optimization problems of general boundary- element models ( BEMs). The evaluation fitness function is performed by a surrogate function based on multidimensional Gaussian random field models ( MGRFMs) in order to minimize the computational cost of the evolutionary algorithms. The model boundary of a structural system or a mechanical tool is discretized using the BEM, and selected parts of the boundary are modelled using beta-spline curves or surfaces in order to facilitate re-meshing and adaptation of the boundary to the external actions. Then a hypercube topology of populations of these models follows a genetic evolution process to determine the optimum shape of the system. The optimum models have minimum weight and satisfy all imposed constraints. A numerical example is presented and discussed in order to show the efficiency and robustness of the developed computational tool. The number of function evaluations is substantially reduced compared with previous versions of the optimization algorithm without the metamodel evaluation technique.

关键词： metamodel evaluation distributed genetic algorithms shape optimization

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REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

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SOFT COMPUTING 2011年第7期15卷 1389-1403页

作者： Lopez, L. Ignacio Bardallo, Juan M. De Vega, Miguel A. Peregrin, Antonio Univ Huelva Dept Informat Technol Palos De La Fra Huelva Spain

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative algorithms in this field.

关键词： Concept learning distributed genetic algorithms Cooperative evolution Multi-modalities

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Designing optimal controllers for nonlinear frames by considering the effect of response feedback

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SCIENTIA IRANICA 2011年第6期18卷 1170-1178页

作者： Joghataie, A. Mohebbi, M. Sharif Univ Technol Dept Civil Engn Struct & Earthquake Engn Grp Tehran Iran Univ Mohaghegh Ardabili Dept Engn Ardebil Iran

The effect of response feedback on designing optimal controllers for wnonlinear frames has been studied. Different combinations of response feedback have been used in the performance index. The Newmark based nonlinear instantaneous optimal control algorithm has been used as the control algorithm in controlling the response of an eight-story bilinear hysteretic frame subjected to white noise excitation and real earthquakes, and controlled by either eight actuators or a single actuator. While the objective has been to minimize the maximum control force for reducing the maximum drift to below the yielding level, the distributed genetic algorithm (DGA) has been used to determine the proper set of weighting matrices in the performance index. Results show that the performance of the active control system depends on the combination of response feedback, where the velocity feedback has been more effective than acceleration and displacement. Also, although using the full feedback of response in the performance index leads to the design of optimal controllers that require the smallest control force, it is costlier, because it requires more online measurements. Finally, it has been concluded that amongst all possibilities, using only velocity feedback can provide the best results regarding the maximum required control force and online measurement, simultaneously. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Active control distributed genetic algorithms Instantaneous optimal control Optimization Nonlinear Response feedback

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Evolutionary parallel and gradually distributed lateral tuning of fuzzy rule-based systems

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EVOLUTIONARY INTELLIGENCE 2009年第1-2期2卷 5-19页

作者： Robles, I. Alcala, R. Benitez, J. M. Herrera, F. Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

The tuning of Fuzzy Rule-Based Systems is often applied to improve their performance as a post-processing stage once an initial set of fuzzy rules has been extracted. This optimization problem can become a hard one when the size of the considered system in terms of the number of variables, rules and, particularly, data samples is big. distributed genetic algorithms are excellent optimization algorithms which exploit the nowadays available parallel hardware (multicore microprocessors and clusters) and could help to alleviate this growth in complexity. In this work, we present a study on the use of the distributed genetic algorithms for the tuning of Fuzzy Rule-Based Systems. To this end, we analyze the application of a specific Gradual distributed Real-Coded genetic Algorithm which employs eight subpopulations in a hypercube topology and local parallelization at each subpopulation. We tested our approach on nine real-world datasets of different sizes and with different numbers of variables. The empirical performance in solution quality and computing time is assessed by comparing its results with those from a highly effective sequential tuning algorithm. The results show that the distributed approach achieves better results in terms of quality and execution time as the complexity of the problem grows.

关键词： genetic fuzzy system Fuzzy rule-based systems distributed genetic algorithms genetic tuning

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