Adaptive discretization for probabilistic model building genetic algorithms

Chen, Chao-Hong; Liu, Wei-Nan; Chen, Ying-ping

doi:10.1145/1143997.1144174

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Finally, some regions of the search space are more densely covered with high quality solutions whereas others contain mostly solutions of low quality; this suggests that some regions require a more dense discretization than others. To deal with these difficulties, various approaches to adaptive discretization were developed using EDAs (Tsutsui, Pelikan, & Goldberg, 2001;Pelikan, Goldberg, & Tsutsui, 2003;Chen, Liu, & Chen, 2006;Suganthan, Hansen, Liang, Deb, Chen, Auger, & Tiwari, 2005;Chen & Chen, 2010). We discuss some of these next.…”

Section: Discretizationmentioning

confidence: 99%

An introduction and survey of estimation of distribution algorithms

Hauschild

Pelikán

2011

Swarm and Evolutionary Computation

419

211

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Section: Discretizationmentioning

confidence: 99%

An introduction and survey of estimation of distribution algorithms

Hauschild

Pelikán

2011

Swarm and Evolutionary Computation

419

211

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“…These code-vectors are later transformed back into the continuous domain by uniformly sampling the bin region each integral code represents. Details about discretization algorithms can be refered to [8], [12].…”

Section: Discretization Algorithms Integrated With Discrete Edasmentioning

confidence: 99%

Use model building on discretization algorithms for discrete EDAs To work on real-valued problems

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Discretization algorithms have been combined with discrete estimation of distribution algorithms (EDAs) to work on real-valued problems. Existing discretization algorithms, such as the fixed-height histogram (FHH) and the split-on-demand (SoD), utilize merely densities of selected chromosomes to build nextgeneration population, and therefore have limited exploration. This paper adds the concept of model building to FHH and SoD to solve these problems. The model utilizes a variety of information from selected chromosomes to improve the abilities of FHH and SoD to identify promising regions for future exploration. Specifically, a model of expected values of selected chromosomes is combined with FHH and SoD to form expected-value FHH and expected-value SoD. The expected-value-discretization algorithms outperform their original versions on an exploration test function as well as the 25 benchmark functions used in the SoD paper. This paper also introduces a model of differential-expected-value of selected chromosomes. The differential-expected-value FHH and differential-expected-value SoD outperform their expected-value versions when tested on the exploration test function and the 25 benchmark functions.

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“…The real-coded ECGA is a new optimization framework, composed of the extended compact genetic algorithm [8] and split-on-demand (SoD) [4], proposed in the study. In this section, we will first give a brief review of ECGA, then describe how SoD discretizes real numbers for ECGA, and introduce the integration of ECGA and SoD.…”

Section: The Real-coded Ecgamentioning

confidence: 99%

“…Particularly, in the present work, we adopt the extended compact genetic algorithm (ECGA) [8] as the back-end optimization engine and the split-on-demand (SoD) technique [4], an adaptive discretization method, as the interface between ECGA and the characteristic determination problem. We employ the proposed framework to tackle three different characteristic determination problems encountered while conducting research on thin-film transistors.…”

Section: Introductionmentioning

confidence: 99%

Characteristic determination for solid state devices with evolutionary computation

Hung

Chen

Zan

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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In this paper, we develop a new optimization framework that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD) to tackle the characteristic determination problem for solid state devices. As most decision variables of characteristic determination problems are real numbers due to the modeling of physical phenomena, and ECGA is designed for handling discrete-type problems, a specific mechanism to transform the variable types of the two ends is in order. In the proposed framework, ECGA is used as an optimization engine, and SoD is adopted as the interface between the engine and the problem. Moreover, instead of one mathematical model with various parameters, characteristic determination is in fact a set of problems of which the mathematical formulations may be very different. Therefore, in this study, we employ the proposed framework on three study cases to demonstrate that the technique proposed in the domain of evolutionary computation can provide not only the high quality optimization results but also the flexibility to handle problems of different formulations.

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Adaptive discretization for probabilistic model building genetic algorithms

Cited by 13 publications

References 13 publications

An introduction and survey of estimation of distribution algorithms

An introduction and survey of estimation of distribution algorithms

Use model building on discretization algorithms for discrete EDAs To work on real-valued problems

Characteristic determination for solid state devices with evolutionary computation

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