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parallel dynamic water supply scheduling in a cluster of computers

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2001年第15期13卷 1281-1302页

作者： Damas, A Salmerón, M Ortega, J Olivares, G Pomares, H Univ Granada Fac Ciencias Dept Comp Architecture & Comp Technol E-18071 Granada Spain

The parallelization of complex planning and control problems arising in diverse application areas in the industrial, services and commercial environments not only allows the determination of control variables in the required times but also improves the performance of the control procedure as more processors are involved in the execution of the parallel program. In this paper we describe a scheduling application in a water supply network to demonstrate the benefits of parallel processing. The procedure we propose combines dynamic programming with genetic algorithms and time series prediction in order to solve problems in which decisions are made in stages, and the states and control belong to a continuous space. Taking into account the computational complexity of these applications and the time constraints that are usually imposed, the procedure has been implemented by a parallel program in a cluster of computers, an inexpensive and widely extended platform that can make parallelism a practical means of tackling complex problems in many different environments. Copyright (C) 2001 John Wiley Sons, Ltd.

关键词： neuro-dynamic programming evolutionary computation parallel genetic algorithms neural networks clusters of computers scheduling of water supply networks

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An island model genetic algorithm for unequal area facility layout problems

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 68卷 151-162页

作者： Palomo-Romero, Juan M. Salas-Morera, Lorenzo Garcia-Hernandez, Laura Univ Cordoba Area Project Engn Rabanales CampusLeonardo Da Vinci Bldg Cordoba 14071 Spain

The Unequal Area Facility Layout Problem (UA-FLP) has been addressed by various methods, including mathematical modelling, heuristic and metaheuristic approaches. Nevertheless, each type of approach presents problems such as premature convergence, lack of diversity, or high computational cost. In this paper, for the first time, an Island Model genetic Algorithm (IMGA) is proposed to solve these subjects in the UA-FLP. The parallel evolution of several populations is used to maintain the population diversity and to obtain a wider sampling of the search space to obtain better quality solutions in fewer generations. Our novel approach was tested with a well-known set of problems taken from the literature and the results were compared with those of previous reports. In most cases, the results obtained by our novel approach improved on the previous results. Additionally, the proposed approach is able to reach good solutions with a wide range of problem sizes and in a reasonable computational time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Unequal area facility layout problem parallel genetic algorithms Island model Flexible bay structure

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Improving flexibility and efficiency by adding parallelism to genetic algorithms

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STATISTICS AND COMPUTING 2002年第2期12卷 91-114页

作者： Alba, E Troya, JM Univ Malaga Dpto Lenguajes & Ciencias Computac E-29071 Malaga Spain

In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a distributed model consisting in a ring of GA islands. The analyzed features are the time complexity, selection pressure, schema processing rates, efficacy in finding an optimum, efficiency, speedup, and resistance to scalability. Besides that, we briefly discuss how the migration policy affects the search. Also, some of the search properties of cellular GAs are investigated. The selected benchmark is a representative subset of problems containing real world difficulties. We often conclude that parallel GAs are numerically better and faster than equivalent sequential GAs. Our aim is to shed some light on the advantages and drawbacks of various sequential and parallel GAs to help researchers using them in the very diverse application fields of the evolutionary computation.

关键词： parallel genetic algorithms distributed GAs cellular GAs PGAs theory PGA parameters influence speedup efficiency scalability

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A divide and conquer method for learning large Fuzzy Cognitive Maps

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FUZZY SETS AND SYSTEMS 2010年第19期161卷 2515-2532页

作者： Stach, Wojciech Kurgan, Lukasz Pedrycz, Witold Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems FCMs were applied in a large number of diverse areas and have already gained momentum due to their simplicity and easiness of use However, these models are usually generated manually, and thus they cannot be applied when dealing with large number of variables In such cases, their development could be significantly affected by the limited knowledge and skills of the designer. In the past few years we have witnessed the development of several methods that support experts in establishing the FCMs or even replace humans by automating the construction of the maps from data. One of the problems of the existing automated methods is their limited scalability. which results in inability to handle large number of variables The proposed method applies a divide and conquer strategy to speed up a recently proposed genetic optimization of FCMs. We empirically contrast several different designs, including parallelized genetic algorithms. FCM-specific designs based on sampling of the input data, and existing Hebbian-based methods. The proposed method. which utilizes genetic algorithm to learn and merge multiple FCM models that are computed from subsets of the original data, is shown to be faster than other genetic algorithm-based designs while resulting in the FCMs of comparable quality We also show that the proposed method generates FCMs of higher quality than those obtained with the use of Hebbian-based methods. (C) 2010 Elsevier B V. All rights reserved.

关键词： Fuzzy Cognitive Maps Qualitative modeling Inductive learning genetic algorithms parallel genetic algorithms

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Accepting Networks of genetic Processors are computationally complete

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THEORETICAL COMPUTER SCIENCE 2012年 456卷 18-29页

作者： Campos, Marcelino Sempere, Jose M. Univ Politecn Valencia Dept Sistemas Informat & Comp Valencia 46022 Spain

We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we prove that the proposed model is computationally complete (it is equivalent to the Turing machine). Hence, it can accept any recursively enumerable language. In addition, we relate the proposed model with (parallel) genetic algorithms or Evolutionary Programs and we set these techniques as decision problem solvers. (C) 2012 Elsevier B.V. All rights reserved.

关键词： parallel genetic algorithms Networks of biologically-inspired processors Computational completeness

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True scores for tartarus with adaptive GAs that evolve FSMs on GPU

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INFORMATION SCIENCES 2020年 525卷 1-15页

作者： Oguz, Kaya Izmir Univ Econ Dept Comp Engn Sakarya Cad 156 Izmir 35330 Turkey

The Tartarus Problem is one of the candidate benchmark problems in evolutionary algorithms. We take advantage of the graphical processing unit (GPU) to improve the results of the software agents that use finite state machines (FSMs) for this benchmark. While doing so we also contribute to the study of the problem on several grounds. Similar to existing studies we use genetic algorithms to evolve FSMs, but unlike most of them we use adaptive operators for controlling the parameters of the algorithm. We show that the actual number of valid boards is not 297,040, but 74,760, because the agent is indifferent to the rotations of the board. We also show that the agent can only come across 383 different combinations, rather than 6561 that is used in the current literature. A final contribution is that we report the first true scores for the agents by testing them with all available 74,760 boards. Our best solution has a mean score of 8.5379 on all boards. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Tartarus problem parallel genetic algorithms GPGPU Finite state machines

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Hybridizing a genetic algorithm with an artificial immune system for global optimization

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ENGINEERING OPTIMIZATION 2004年第5期36卷 607-634页

作者： Coello, CAC Cortés, NC Inst Politecn Nacl 2508 CINVESTAV IPN Evolutionary Computat Grp Dept Ingn ElectSecc Computac Mexico City 07300 DF Mexico

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.

关键词： artificial immune system genetic algorithms global optimization parallel genetic algorithms

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parallel evolutionary modelling of geological processes

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parallel COMPUTING 2007年第3期33卷 186-212页

作者： D'Ambrosio, D. Spataro, W. Univ Calabria Dept Math I-87036 Arcavacata Di Rende Italy Univ Calabria High Performance Comp Ctr I-87036 Arcavacata Di Rende Italy

This paper illustrates a parallel computing methodology for modelling and simulating geological processes by means of Cellular Automata and parallel genetic algorithms. Two models, concerning lava and debris flows, have been implemented using the Cellular Automata development environment CAMELot, and calibrated by means of genetic algorithms through the PGAPack library. Experiments have been carried out on two different distributed memory machines, namely a Beowulf cluster and a HP Alphaserver SC supercomputer. Results have demonstrated the goodness of both considered geological models and of the genetic algorithm employed for their calibration. High computational performances have been achieved. In particular, results obtained for the calibration phase demonstrated that even low-cost parallel machines can be fruitfully employed for the construction of reliable simulation models for geological processes. (C) 2007 Elsevier B.V. All rights reserved.

关键词： cellular automata parallel genetic algorithms geological processes simulation optimisation parallel processing

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parallel multiobjective memetic RBFNNs design and feature selection for function approximation problems

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NEUROCOMPUTING 2009年第16-18期72卷 3541-3555页

作者： Guillen, A. Pomares, H. Gonzalez, J. Rojas, I. Valenzuela, O. Prieto, B. Univ Granada Dept Comp Technol & Architecture E-18071 Granada Spain

The design of radial basis function neural networks (RBFNNs) still remains as a difficult task when they are applied to classification or to regression problems. The difficulty arises when the parameters that define an RBFNN have to be set, these are: the number of RBFs, the position of their centers and the length of their radii. Another issue that has to be faced when applying these models to real world applications is to select the variables that the RBFNN will use as inputs. The literature presents several methodologies to perform these two tasks separately, however, due to the intrinsic parallelism of the genetic algorithms, a parallel implementation will allow the algorithm proposed in this paper to evolve solutions for both problems at the same time. The parallelization of the algorithm not only consists in the evolution of the two problems but in the specialization of the crossover and mutation operators in order to evolve the different elements to be optimized when designing RBFNNs. The subjacent genetic algorithm is the non-sorting dominated genetic algorithm II (NSGA-II) that helps to keep a balance between the size of the network and its approximation accuracy in order to avoid overfitted networks. Another of the novelties of the proposed algorithm is the incorporation of local search algorithms in three stages of the algorithm: initialization of the population, evolution of the individuals and final optimization of the Pareto front. The initialization of the individuals is performed hybridizing clustering techniques with the mutual information (MI) theory to select the input variables. As the experiments will show, the synergy of the different paradigms and techniques combined by the presented algorithm allow to obtain very accurate models using the most significant input variables. (C) 2009 Published by Elsevier B.V.

关键词： parallel genetic algorithms RBF Neural networks Function approximation RBFNN MPI

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A parallel PROBABILISTIC SYSTEM-LEVEL FAULT DIAGNOSIS APPROACH FOR LARGE MULTIPROCESSOR SYSTEMS

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parallel PROCESSING LETTERS 2006年第1期16卷 63-79页

作者： Elhadef, Mourad Abrougui, Kaouther Das, Shantanu Nayak, Amiya Univ Ottawa Sch Informat Technol & Engn Ottawa ON Canada

In this paper, we present a system-level fault identification algorithm, using a parallel genetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model where individual node fails with an a priori probability p. The assumptions concerning test outcomes are the same as in the PMC model, that is, fault-free testers always give correct test outcomes and faulty testers are totally unpredictable. The parallel diagnosis algorithm was implemented and simulated on randomly generated large systems. The proposed parallelization is intended to speed up the performance of the evolutionary diagnosis approach, hence reducing the computation time by evolving various sub-populations in parallel. Simulation results are provided showing that the parallel diagnosis did improve the efficiency of the evolutionary diagnosis approach, in that it allowed faster diagnosis of faulty situations, making it a viable alternative to existing techniques of diagnosis. Moreover, the evolutionary approach still provide good results even when extreme non-diagnosable faulty situations are considered.

关键词： Fault tolerance System-level diagnosis Multiprocessor systems Probabilistic diagnosis parallel genetic algorithms parallel virtual machine (PVM)

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