High-Performance Hardware Design and Implementation of Genetic Algorithms

Shackleford, Barry; Okushi, Etsuko; Yasuda, Mitsuhiro; Koizumi, Hiroyuki; Seo, Katsuhiko; Iwamoto, Takahashi; Yasuura, Hiroto

doi:10.1007/978-3-7908-1816-1_2

Cited by 11 publications

(14 citation statements)

References 8 publications

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“…The H/W processor for implementing the S/W simulation should be developed to recognize the pattern during unstable communication status due to noise and also take the measures for it in real-time. [7,8,9] Advanced Engineering Forum Vols. 2-3(a) size=50, Iteration=100 (b) size=70, Iteration=100 (c) size=90, Iteration=100…”

Section: Performance and Results Of The Noise Pattern Recognitionmentioning

confidence: 99%

A Research on the Extraction and Interpretation of Power Line Communication Noise Pattern Using Genetic Algorithm

Seo¹,

Jeong

Chung³

2011

AEF

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Abstract.A PLC is more sensitive in noise than other wired communication system that is cable, DSL, and optical LAN. Therefore it considered as a crucial point in commercialization of PLC. For the commercialization of PLC, noise reduction and cancelation technique are needed. Therefore, it is definitely required to analyze and interpret the effects and types of the noise, exactly. The purpose of this work is to separate and extract the various noise of power line from data signals.

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Section: Performance and Results Of The Noise Pattern Recognitionmentioning

confidence: 99%

A Research on the Extraction and Interpretation of Power Line Communication Noise Pattern Using Genetic Algorithm

Seo¹,

Jeong

Chung³

2011

AEF

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“…At Hewlett-Packard Laboratories, a high-performance hardware implementation of a genetic algorithm is being designed [64]. A 2200x speed-up over software emulation on a 100MHx workstations was reached.…”

Section: Future Workmentioning

confidence: 99%

Genetic algorithms applied to nonlinear and complex domains

Barash¹,

Woodin²

1999

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The dissertation, titled "Genetic Algorithms Applied to Nonlinear and Complex Domains", describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a 'final' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas.First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t ,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable,

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“…In [1] and [2], survival-based steady-state GA and Compact Genetic Algorithm are used to reduce sizes of a hardware circuit and memory, respectively.…”

Section: B Generation Model Of Hardware Gamentioning

confidence: 99%

“…In [10], hardware GA called H 3 engine that adopts steady-state GA was implemented. In [1], Barry et al developed hardware circuits for Set Coverage Problem using Steady-state GA. In [2], Aporntewan et al proposed a hardware implementation technique for Compact Genetic Algorithm on FPGAs.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

Tachibana

Murata²,

Shibata³

et al.

2006 IEEE International Conference on Evolutionary Computation

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Multi-Objective Genetic Algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method.

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High-Performance Hardware Design and Implementation of Genetic Algorithms

Cited by 11 publications

References 8 publications

A Research on the Extraction and Interpretation of Power Line Communication Noise Pattern Using Genetic Algorithm

A Research on the Extraction and Interpretation of Power Line Communication Noise Pattern Using Genetic Algorithm

Genetic algorithms applied to nonlinear and complex domains

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

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