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Influence of the migration policy in parallel distributed GAs with structured and panmictic populations

APPLIED INTELLIGENCE 2000年第3期12卷 163-181页

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

parallel genetic algorithms (PGAs) have been traditionally used to overcome the intense use of CPU and memory that serial GAs show in complex problems. Non-parallel GAs can be classified into two classes: panmictic and structured-population algorithms. The difference lies in whether any individual in the population can mate with any other one or not. In this work, they are both considered as two reproductive loop types executed in the islands of a parallel distributed GA. Our aim is to extend the existing studies from more conventional sequential islands to other kinds of evolution. A key issue in such a coarse grain PGA is the migration policy, since it governs the exchange of individuals among the islands. This paper investigates the influence of migration frequency and migrant selection in a ring of islands running either steady-state, generational, or cellular GAs. A diversity analysis is also offered from an entropy point of view. The study uses different problem types, namely easy, deceptive, multimodal, NP-Complete, and epistatic search landscapes in order to provide a wide spectrum of problem difficulties to support the results. Large isolation values and random selection of the migrants are demonstrated as providing a larger probability of success and a smaller number of visited points. Also, interesting observations on the relative performance of the different models are offered, as well as we point out the considerable benefits that can accrue from asynchronous migration.

关键词： parallel genetic algorithms complex search spaces migration policy entropy basic island evolution

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Evolving the neural network model for forecasting air pollution time series

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第2期17卷 159-167页

作者： Niska, H Hiltunen, T Karppinen, A Ruuskanen, J Kolehmainen, M Univ Kuopio Dept Environm Sci FIN-70211 Kuopio Finland Finnish Meteorol Inst FIN-00880 Helsinki Finland

The modelling of real-world processes such as air quality is generally a difficult task due to both their chaotic and non-linear phenomenon and high dimensional sample space. Despite neural networks (NN) have been used successfully in this domain, the selection of network architecture is still problematic and time consuming task when developing a model for practical situation. This paper presents a study where a parallel genetic algorithm (GA) is used for selecting the inputs and designing the high-level architecture of a multi-layer perceptron model for forecasting hourly concentrations of nitrogen dioxide at a busy urban traffic station in Helsinki. In addition, the tuning of GA's parameters for the problem is considered in experimental way. The results showed that the GA is a capable tool for tackling the practical problems of neural network design. However, it was observed that the evaluation of NN models is a computationally expensive process, which set limits for the search techniques. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： feed-forward networks time series forecasting parallel genetic algorithms urban air pollution

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Research on Intelligent Scheduling of Foreign Flight Attendants' busy time Based on parallel genetic Algorithm 22

Research on Intelligent Scheduling of Foreign Flight Attenda...

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Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence

作者： Hong Ling Wuhan Business University China

ISBN: (纸本)9781450396882

The current intelligent scheduling method of foreign service staff based on intelligent optimization algorithm realizes the scheduling of staff by individual coding, which leads to the low stability of the algorithm because the constraints and optimization of the objective function are not comprehensive enough. In this regard, the intelligent scheduling method of foreign service staff of busy time airlines based on parallel genetic algorithm is proposed. By setting the optimal parameters such as population size, the objective function is constructed with the airline operation revenue and passenger service degree as the objectives, and the shallow copy of data is used to optimize and constrain the function, and the outbound flight service staff scheduling model is constructed. In the experiment, the stability of the proposed intelligent scheduling method is verified. The analysis of the experimental results shows that the objective function constructed by using the proposed method has a high degree of convergence and high stability.

关键词： Foreign Airline Service Personnel Personnel Scheduling Shallow Copies of Data parallel genetic algorithms Busy Time

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A parallel genetic algorithm for the open-shop scheduling problem using deterministic and random moves 09

A parallel genetic algorithm for the open-shop scheduling pr...

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Proceedings of the 2009 Spring Simulation Multiconference

作者： Haidar M. Harmanani Fouad Drouby Steve Bou Ghosn Lebanese American University Byblos Lebanon

This paper investigates the use of parallel genetic algorithms in order to solve the open-shop scheduling problem. The method is based on an interesting implementation of genetic operators that combines the use of deterministic and random moves. The method is implemented using MPI on a Beowulf cluster. Favorable results comparisons using the Taillard benchmarks are reported.

关键词： parallel genetic algorithms open-shop scheduling

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An improved evolutionary algorithm as function optimizer

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IETE JOURNAL OF RESEARCH 2000年第1-2期46卷 47-56页

作者： Bandyopadhyay, S Maulik, U Indian Stat Inst Machine Intelligence Unit Calcutta 700035 W Bengal India Govt Engn Coll Dept Comp Sci Kalyani Nadia India

In this paper an efficient evolutionary search algorithm, which can exploit the enhanced searching capability of parallel genetic algorithms while being sequential in nature, has been proposed. The performance of the algorithm with and without migration of chromosomes has been studied. The necessity of information exchange, in the form of migration between different processes, for good performance of the proposed algorithm is adequately established for a variety of function optimization problems. Variation of performance of the proposed algorithm with the number of migrations is demonstrated experimentally;thereby indicating that a proper choice of the frequency of migration and number of processes are crucial.

关键词： chromosome genetic algorithm migration optimization parallel genetic algorithms

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A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithms

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 116卷 234-252页

作者： Garcia Valdez, Mario Merelo Guervos, Juan J. Inst Tecnol Tijuana Dept Grad Studies Tijuana Bc Mexico Univ Granada Dept Comp Architecture & Technol Granada Spain

Splitting a population into multiple instances is a technique used extensively in recent years to help improve the performance of nature-inspired optimization algorithms. Work on those populations can be done in parallel, and they can interact asynchronously, a fact that can be leveraged to create scalable implementations based on, among other methods, distributed, multi-threaded, parallel, and cloud-native computing. However, the design of these cloud-native, distributed, multi-population algorithms is not a trivial task. Using as a foundation monolithic (single-instance) solutions, adaptations at several levels, from the algorithmic to the functional, must be made to leverage the scalability, elasticity, (limited) fault-tolerance, reproducibility, and cost-effectiveness of cloud systems while, at the same time, conserving the intended functionality. Instead of an evolutive approach, in this paper, we propose a cloud-native optimization framework created from scratch, that can include multiple (population-based) algorithms without increasing the number of parameters that need tuning. This solution goes beyond the current state of the art, since it can support different algorithms at the same time, work asynchronously, and also be readily deployable to any cloud platform. We evaluate this solution's performance and scalability, together with the effect other design parameters had on it, particularly the number and the size of populations with respect to problem size. The implemented platform is an excellent alternative for running locally or in the cloud, thus proving that cloud-native bioinspired algorithms perform better in their "natural" environment than other algorithms, and set a new baseline for scaling and performance of this kind of algorithms in the cloud. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-population Nature-inspired algorithm parallel genetic algorithms Cloud-computing Event-driven architecture

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Efficient Distributed genetic Algorithm for Rule extraction

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APPLIED SOFT COMPUTING 2011年第1期11卷 733-743页

作者： Rodriguez, Miguel Escalante, Diego M. Peregrin, Antonio Univ Huelva Dept Informat Technol Huelva 21819 Spain

This paper presents an Efficient Distributed genetic Algorithm for classification Rule extraction in data mining (EDGAR), which promotes a new method of data distribution in computer networks. This is done by spatial partitioning of the population into several semi-isolated nodes, each evolving in parallel and possibly exploring different regions of the search space. The presented algorithm shows some advantages when compared with other distributed algorithms proposed in the specific literature. In this way, some results are presented showing significant learning rate speedup without compromising the accuracy. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Classification rules Rule induction Distributed computing Coarse-grained implementation parallel genetic algorithms

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Accelerating genetic algorithms with GPU computing: A selective overview

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 514-525页

作者： Cheng, John Runwei Gen, Mitsuo Broad Geophys Technol Inc 10630 Haddington Dr Houston TX 77043 USA Tokyo Univ Sci Res Inst Sci & Technol Tokyo Japan Fuzzy Log Syst Inst Fukuoka Fukuoka Japan

The emergence of GPU-CPU heterogeneous architectures has led to a fundamental paradigm shift in parallel programming. Accelerating genetic algorithms (GAs) on these architectures has received significant attention from both practitioners and researchers ever since GPUs emerged. In the past decade we have witnessed many progresses on migrating parallel GM from CPU to GPU (Graphical Processing Unit) architecture, which makes this research field truly enter into the world of High Performance Computing (HPC), and demonstrates a great potential to many research disciplines and industrial worlds that can benefit from the power of GPU accelerated stochastic global search to explore large and complex search spaces for better solutions. Designing a parallel algorithm on GPU is quite different from designing one on CPU. On CPU architecture, we typically consider how to distribute data across tens of CPU threads, while on GPU architecture, we have more than hundreds of thousands of GPU threads running simultaneously. Therefore, we should rethink the design approaches and implementation strategies of parallel algorithms to fully utilize the computing power of GPUs to accelerate the computation of GAs. The intention of this paper is to give an overview on selective works of parallel GM designed for GPU architecture. In this survey paper, we first reexamine the concept of granularity of parallelism for GM on GPU architecture, discuss how the aspect of data layout affect the kernel design to maximize memory bandwidth, and explain how to organize threads in grid and blocks to expose sufficient parallelism to GPU. The comprehensive overview on selective works since 2010 then follows. The focus is mainly on the perspective of GPU architecture: how to accelerate GAs with GPU computing. Performance issues are not touched in this review, because most of these works are conducted on very early GPU cards, which are out of date already. We finally discuss some future research suggestions i

关键词： parallel genetic algorithms GPU computing parallelism

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parallelisation of genetic algorithms for the 2-page crossing number problem

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2007年第2期67卷 229-241页

作者： He, Hongmei Sykora, Ondrej Salagean, Ana Makinen, Erkki Univ Loughborough Dept Comp Sci Loughborough LE11 3TU Leics England Univ Tampere Dept Comp Sci FIN-33014 Tampere Finland

genetic algorithms (GAs) have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. parallelisation provides an attractive prospect to improve the efficiency and solution quality of GAs. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: computation time to communication time and population size to chromosome size. Moreover, the paper unifies the framework of PGA models with the function PGA (subpopulation size, cluster size, migration period, topology), and explores the performance of PGAs for the 2-page crossing number problem. (C) 2006 Elsevier Inc. All rights reserved.

关键词： 2-page crossing number parallel genetic algorithms evaluation measures

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Structural damage assessment under varying temperature conditions

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2012年第3期11卷 345-357页

作者： Meruane, V. Heylen, W. Univ Chile Dept Mech Engn Santiago Chile Katholieke Univ Leuven Dept Mech Engn B-3001 Heverlee Belgium

Modal parameters such as natural frequencies and mode shapes are sensitive indicators of structural damage. However, they are not only sensitive to damage, but also to the environmental conditions such as, humidity, wind and most important, temperature. For civil engineering structures, modal changes produced by environmental conditions can be equivalent or greater than the ones produced by damage. This article proposes a damage detection method which is able to deal with temperature variations. The objective function correlates mode shapes and natural frequencies, and a parallel genetic algorithm handles the inverse problem. The numerical model of the structure assumes that the elasticity modulus of the materials is temperature-dependent. The algorithm updates the temperature and damage parameters together. Therefore, it is possible to distinguish between temperature effects and real damage events. Simulated data of a three-span bridge and experimental one of the I-40 Bridge validate the proposed methodology. Results show that the proposed algorithm is able to assess the experimental damage despite of temperature variations.

关键词： damage assessment temperature variations parallel genetic algorithms modal data I-40 bridge

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