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10th International Conference on Computational Collective Intelligence (ICCCI)

作者： Poveda, Roberto Gomez, Jonatan Univ Dist Francisco Jose de Caldas Fac Engn Bogota Colombia Univ Nacl Colombia Dept Comp Engn Bogota Colombia

ISBN: (纸本)9783319984469;9783319984452

This paper presents a fine-grained parallel genetic algorithm improved with a 2-opt heuristic for finding solutions near to the optimum to the Quadratic Assignment Problem (QAP). The proposed algorithm is fully implemented on Graphics Processing Units (GPUs). Unlike previous approaches reported in the literature our implementation a two-dimensional GPU grid of size 10x10 defines the population of the genetic algorithm (set of permutations of the QAP) and each GPU block consists of n GPU threads where n is the size of QAP. Each GPU block is used to represent the chromosome of a single individual and each GPU thread represents a gene of such chromosome. The proposed algorithm is tested on a subset of the standard QAPLIB data set. Our results show that our implementation is able to find good solutions for large QAP instances in few parallel iterations of the evolutionary process.

关键词： Quadratic Assignment Problem parallel genetic algorithms Graphics Processing Units

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A scalable parallel genetic algorithm for x-ray spectroscopic analysis 05

A scalable parallel genetic algorithm for x-ray spectroscopi...

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genetic and Evolutionary Computation Conference

作者： Xu, Kai Louis, Sushil J. Mancini, Roberto C. Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

ISBN: (纸本)1595930108

We use a parallel multi-objective genetic algorithm to drive a search and reconstruction spectroscopic analysis of plasma gradients in inertial confinement fusion (ICF) implosion cores. In previous work, we had shown that our serial multi-objective genetic Algorithm was a good method to solve two-criteria X-ray spectroscopy diagnostics problems. However, this serial version was slow and we therefore could not incorporate better physics and more criteria to solve larger problems and handle larger data sets. In this paper, we develop and use a parallel multi-objective genetic algorithm based on a master-slave model to solve three criteria spectroscopic analysis problems. The algorithm works well in reconciling experimental observations with theoretical physics model parameters. In addition, theoretical analysis and experimental results on the parallelized version show good scalability with up to 150 processors. This reduces the time for running the GA from 9.6 hours to 5.9 minutes.

关键词： parallel genetic algorithms speedup X-ray spectroscopy

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parallel genetic ALGORITHM ON PVM

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Wuhan University Journal of Natural Sciences 1996年第Z1期1卷 605-610页

作者： Cuangming Lin Xin Yao(Department of Computer Science, Computational Intelligence GroupUniversity College, The University of New South Wales, ADFA)lain Macleod(Computer Sciences Lab RSISE ANUCanberra, ACT, Australia 2600)Lishan Kang Yuping Chen(Institu Department of Computer Science Computational Intelligence Group University College The University of New South Wales ADFA Australia Computer Sciences Lab. RSISE ANU Canberra Australia Institute of Software Engineering Wuhan University Wuhan P.R.China

In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM,parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running On many small subpopulations with an occasional identification and exchange of their useful information among subpopulations by means of message-passing functions of PVM. In this work, experiments were done to compare the parallel genetic algorithm and traditional sequential genetic algorithms.

关键词： parallel genetic algorithms parallel Virtual Machine(PVM) Island Model Cellular Model.

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Hadoop MapReduce for parallel genetic Algorithm to Solve Traveling Salesman Problem

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第8期10卷 97-107页

作者： Manzi, Entesar Bennaceur, Hachemi Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Dept Comp Sci Riyadh Saudi Arabia

Achieving an optimal solution for NP-complete problems is a big challenge nowadays. The paper deals with the Traveling Salesman Problem (TSP) one of the most important combinatorial optimization problems in this class. We investigated the parallel genetic Algorithm to solve TSP. We proposed a general platform based on Hadoop MapReduce approach for implementing parallel genetic algorithms. Two versions of parallel genetic algorithms (PGA) are implemented, a parallel genetic Algorithm with Islands Model (IPGA) and a new model named an Elite parallel genetic Algorithm using MapReduce (EPGA) which improve the population diversity of the IPGA. The two PGAs and the sequential version of the algorithm (SGA) were compared in terms of quality of solutions, execution time, speedup and Hadoop overhead. The experimental study revealed that both PGA models outperform the SGA in terms of execution time, solution quality when the problem size is increased. The computational results show that the EPGA model outperforms the IPGA in term of solution quality with almost similar running time for all the considered datasets and clusters. genetic algorithms with MapReduce platform provide better performance for solving large-scale problems.

关键词： genetic algorithms parallel genetic algorithms Hadoop MapReduce island model traveling salesman problem

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Speed up genetic algorithms in the cloud using software containers

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 276-289页

作者： Salza, Pasquale Ferrucci, Filomena USI Univ Svizzera Italiana Fac Informat Lugano Switzerland Univ Salerno Dept Comp Sci Salerno Italy

Scalability issues might prevent genetic algorithms from being applied to real world problems. Exploiting parallelisation in the cloud might be an affordable approach to getting time efficient solutions that benefit of the appealing features of scalability, resource discovery, reliability, fault-tolerance and costeffectiveness. Nevertheless, parallel computation is very prone to cause considerable overhead for communication. Also, making genetic algorithms distributed in an on-demand fashion is not trivial. Aiming at keeping under control the communication cost and, at the same time, supporting developers in the construction and deployment of parallel genetic algorithms, we designed and implemented a novel approach to distribute genetic algorithms in the form of a cloud-based application. It is based on the master/slave model, exploiting software containers, their cloud orchestration and message queues. We also devised a conceptual workflow covering each cloud genetic algorithms distribution phase, from resources allocation to actual deployment and execution, in an engineered fashion. Then, the application performance has been evaluated using a benchmark problem. According to the performance and setup times results, it emerged that the cloud can be considered a compelling way of scaling genetic algorithms and an excellent alternative to other technologies strictly related to the physically owned hardware. (C) 2018 Elsevier B.V. All rights reserved.

关键词： genetic algorithms parallel genetic algorithms Cloud computing Software containers Software engineering

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Cross breeding in genetic optimisation and its application to fuzzy logic controller design

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ARTIFICIAL INTELLIGENCE IN ENGINEERING 1998年第1-2期12卷 15-20页

作者： Pham, DT Karaboga, D Univ Wales Coll Cardiff Intelligent Syst Res Lab Cardiff CF2 1XH S Glam Wales

This paper describes a simple genetic algorithm (GA) based on the idea of cross breeding 'chromosomes' from different solution populations. The proposed genetic algorithm has been tested against a standard GA on the problem of designing fuzzy logic controllers. The paper presents the simulation results obtained for the control of a second-order time-delayed plant. These demonstrate the effectiveness of the proposed algorithm. (C) 1997 Elsevier Science Limited.

关键词： genetic algorithms parallel genetic algorithms fuzzy logic controller design

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USING TRANSPUTERS TO INCREASE SPEED AND FLEXIBILITY OF geneticS-BASED MACHINE LEARNING-SYSTEMS

MICROPROCESSING AND MICROPROGRAMMING

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MICROPROCESSING AND MICROPROGRAMMING 1992年第1-5期34卷 147-152页

作者： DORIGO, M POLITECN MILAN DIPARTIMENTO ELETTRI-20133 MILANITALY

We implemented a distributed environment for machine learning experimentation on a transputer network. The system can be used by a researcher to build modular and efficient learning systems. The algorithms composing the basic structure of the implementation are the genetic algorithm, the bucket brigade algorithm and the inferential engine. We present a parallel version of these algorithms and call it low-level parallelism. Compared to the standard sequential version of the same algorithms, low-level parallelism gives us an increase in performance. To provide the learning system designer with a higher level of flexibility than currently available with standard systems, we also implemented high-level parallelism: subsets of the transputer network can be allocated to different learning systems. In this way a complex learning problem can be decomposed in many simpler problems, each one mapped on a single (possibly low-level parallel) learning system.

关键词： parallel genetic algorithms IMPLEMENTATION ON TRANSPUTERS geneticS-BASED MACHINE LEARNING

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Generating networks of genetic processors

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genetic PROGRAMMING AND EVOLVABLE MACHINES 2022年第1期23卷 133-155页

作者： Campos, Marcelino Sempere, Jose M. Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Valencia Spain

The Networks of genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.

关键词： Natural computing Networks of bio-inspired processors parallel genetic algorithms Formal languages Descriptive complexity

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Two Distributed algorithms for the Frequency Assignment Problem in the Field of Radio Broadcasting

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IEEE TRANSACTIONS ON BROADCASTING 2009年第2期55卷 223-229页

作者： Idoumghar, Lhassane Schott, Rene Univ Haute Alsace LMIA F-68093 Mulhouse France IECN F-54506 Vandoeuvre Les Nancy France LORIA F-54506 Vandoeuvre Les Nancy France

The frequency assignment problem involves the assignment of discrete frequencies to the transmitters of a radio network, such as a radio broadcasting network. Frequency separation is necessary to avoid interference by other transmitters to the signal received from the wanted transmitter at the reception region. Here, it is of major importance to minimize the interference while at the same time using the spectrum efficiently. In this paper we present two original distributed algorithms implemented on clusters of PCs used to solve the frequency assignment problem in the field of radio broadcasting. The first one is based on the island distributed [1] implementation of our hybrid genetic algorithm [2]. The second one uses a distributed cooperative Tabu Search. Experimental results show that our algorithms, applied to several instances given by TDF-C2R, lead to important time performance improvements.

关键词： Cooperative tabu search algorithm frequency assignment problem hybrid algorithms parallel genetic algorithms radio broadcasting

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genetic neural networks to approximate feedback nash equilibria in dynamic games

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2003年第10-11期46卷 1493-1509页

作者： Sirakaya, S Alemdar, NM Univ Washington Dept Econ Seattle WA 98195 USA Univ Washington Dept Stat Seattle WA 98195 USA Univ Washington Ctr Stat & Social Sci Seattle WA 98195 USA Bilkent Univ Dept Econ TR-06800 Ankara Turkey

This paper develops a general purpose numerical method to compute the feedback Nash equilibria in dynamic games. Players' feedback strategies are first approximated by neural networks which are then trained online by parallel genetic algorithms to search over all time-invariant equilibrium strategies synchronously. To eliminate the dependence of training on the initial conditions of the game, the players use the same stationary feedback policies (the same networks), to repeatedly play the game from a number of initial states at any generation. The fitness of a given feedback strategy is then computed as the sum of payoffs over all initial states. The evolutionary equilibrium of the game between the genetic algorithms is the feedback Nash equilibrium of the dynamic game. An oligopoly model with investment is approximated as a numerical example. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： feedback nash equilibrium parallel genetic algorithms neural networks

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