A convergence model for asynchronous parallel genetic algorithms

Berntsson, Johan; Tang, Maolin

doi:10.1109/cec.2003.1299419

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…It has been shown that superlinear speedup can be attained using this method, as smaller populations can converge to minima quicker than larger populations [10], [11]. This approach is well suited to clusters and grids as inter-population communication can reduce the effect of high latency connections [12], [13].…”

Section: A Distributed Genetic Searchmentioning

confidence: 97%

Robust Asynchronous Optimization for Volunteer Computing Grids

Desell

Magdon‐Ismail

Szymanski

et al. 2009

2009 Fifth IEEE International Conference on E-Science

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Abstract-Volunteer computing grids offer significant computing power at relatively low cost to researchers, while at the same time generating public interest in different scientific projects. However, in order to be used effectively, their heterogeneity, volatility and restrictive computing models must be overcome. As these computing grids are open, incorrect or malicious results must also be handled. This paper examines extending the BOINC volunteer computing framework to allow for asynchronous global optimization as applied to scientific computing problems. The asynchronous optimization method used is resilient to faults and the heterogeneous nature of volunteer computing grids, while allowing scalability to tens of thousands of hosts. A work verification strategy that does not require the validation of every result is presented. This is shown to be able to effectively reduce the need for verification done to less than 30% of the reported results, without degrading the performance of the asynchronous search methods. An asynchronous version of particle swarm optimization (APSO) is presented and compared to previously used asynchronous genetic search (AGS) using the MilkyWay@Home BOINC computing project. Both search methods are shown to scale to MilkyWay@Home's current user base, over 75,000 heterogeneous and volatile hosts, something not possible for traditional optimization methods. APSO is shown to provide faster convergence to optimal results while being less sensitive to its search parameters. The verification strategy presented is shown to be effective for both AGS and APSO.

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Section: A Distributed Genetic Searchmentioning

confidence: 97%

Robust Asynchronous Optimization for Volunteer Computing Grids

Desell

Magdon‐Ismail

Szymanski

et al. 2009

2009 Fifth IEEE International Conference on E-Science

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“…The results showed that asynchronous island GA is always more efficient than synchronous island GA. Berntsson and Tang investigated the convergence of populations in asynchronous island GA [3]. They showed that convergence can be speeded up by making immigration more frequent.…”

Section: Related Workmentioning

confidence: 97%

“…But A-SAIGA does not synchronize between islands, and this may somewhat affect the search performance. But it is known that if immigration is frequent, the populations of the islands becomes similar in asynchronous Island GA with different numbers of evaluations for each island [3]. Thus, the influence of eliminating synchronization can be minimized by making immigration frequent.…”

Section: A-saigamentioning

confidence: 98%

Techniques to improve exploration efficiency of parallel self‐adaptive genetic algorithms by dispensing with iteration and synchronization

Takashima

Murata

Shibata

et al. 2006

Systems & Computers in Japan

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SUMMARYThe exploration efficiency of GAs depends on parameter values such as the mutation rate and crossover rate. To save the labor of manually adjusting these values, GAs which automatically adjust parameters (adaptive GAs) have been proposed. However, most of the existing adaptive GAs can adjust only a few parameters simultaneously. Although several adaptive GAs can adjust many parameters simultaneously, these algorithms have a large computational cost.In this paper, we propose the Self-Adaptive Island GA (SAIGA) and its asynchronous implementation Asynchronous SAIGA (A-SAIGA). These two GAs are combinations of Meta GA and Island GA, and can adapt many parameters simultaneously with a computational cost equivalent to that of the simple GA. A-SAIGA improves exploration speed by avoiding synchronization between islands.Throughout our evaluation experiments, we confirmed that the performance of these GAs is close to that of the simple GA with optimal parameters. We also confirmed that A-SAIGA outperforms SAIGA in exploration speed.

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“…Regarding parallel processing, examples of parallel GA have been proposed by J.Berntsson et al [4], M.Golub et al [5], and so on. J.…”

Section: Related Workmentioning

confidence: 99%

Dedicated Hardware Architecture for Partially Mapped Crossover

Yoshikawa

Terai

2008

2008 19th International Conference on Systems Engineering

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This paper discusses new dedicated hardware architecture for partially mapped crossover (PMX). The proposed architecture introduces a new PMX algorithm which is suitable for hardware. It creates the matching-table using a register as preprocessing. The matching-table reduces the number of steps for comparison processing which is required in PMX algorithm. Thus, the proposed architecture requires only one clock cycle for the comparison processing, and achieves the high speed processing. Simulation results show that the proposed architecture achieved five times more high-speed processing than conventional PMX on average.

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A convergence model for asynchronous parallel genetic algorithms

Cited by 11 publications

References 5 publications

Robust Asynchronous Optimization for Volunteer Computing Grids

Robust Asynchronous Optimization for Volunteer Computing Grids

Techniques to improve exploration efficiency of parallel self‐adaptive genetic algorithms by dispensing with iteration and synchronization

Dedicated Hardware Architecture for Partially Mapped Crossover

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