Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and Application

Ahn, Chang Wook; Goldberg, David E.; Ramakrishna, R. S.

doi:10.1007/978-3-540-24669-5_71

Cited by 16 publications

(19 citation statements)

References 4 publications

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“…Assuming a fixed number k of clusters and a fixed number p of genes, a total of p · k independent binomial proportions would be kept updated during the evolutionary process. New individuals would be generated by sampling from the probability vector of a single cluster, or combination among clusters (interbreeding), similarly as in [22].…”

Section: Related Workmentioning

confidence: 99%

“…These first experiments illustrates the behavior of the cg-combination, which is compared to the PV-wise uniform crossover [22]. This later operator randomly mixes two building blocks during an interbreeding, while the cg-combination carefully chooses from which parent to take each position of the probability vector, as described in Section 4.…”

Section: Comparing Two Interbreeding Mechanismsmentioning

confidence: 99%

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Effective Linkage Learning Using Low-Order Statistics and Clustering

Emmendorfer

Pozo²

2009

IEEE Trans. Evol. Computat.

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Section: Related Workmentioning

confidence: 99%

Section: Comparing Two Interbreeding Mechanismsmentioning

confidence: 99%

Effective Linkage Learning Using Low-Order Statistics and Clustering

Emmendorfer

Pozo²

2009

IEEE Trans. Evol. Computat.

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“…In [25], the theory of population sizing and timing to convergence is published. A new idea of the multideme parallel estimation of distribution algorithm (PEDAs) based on PBIL algorithm was published in [1]. In [16], mixtures of distribution with Bayesian inference are discussed.…”

Section: Traditional Edasmentioning

confidence: 99%

“…The main goal is to find a robust computational tool for hard optimization problems. The present approaches recently published in [1,8,9] use a simpler probability model only (PBIL, UMDA).…”

Section: Migration Of the Probabilistic Modelmentioning

confidence: 99%

Parallel BMDA with probability model migration

Jaroš

Schwarz

2007

2007 IEEE Congress on Evolutionary Computation

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Summary. The chapter presents a new concept of parallel BivariateMarginal Distribution Algorithm (BMDA) using the stepping stone communication model with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new adaptive BMDA (aB-MDA) algorithms is to modify the classic learning of the probability model (applied in the sequential BMDA [24]). In the proposed strategy, the adaptive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson's chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. Experimental results show that the proposed adaptive aBMDA significantly outperforms the traditional concept of migration of individuals.

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“…CCGA is developed from cooperative compact genetic algorithms [14]. In addition, Cellular CGA is also derived from the parallel EDAs [17][18][19]. Due to Cellular CGA employs two dimensional array structure like cellular automata, it is suitable for FPGA and hardware implementation [25].…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of a cellular univariate estimation of distribution algorithm and block-based neural network as an evolvable hardware

Jewajinda

Chongstitvatana

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-This paper pr esents a har dwar e implementation of evolvable block-based neur al networ k (BBNN) amd a kind of EDAs called cellular compact genetic algor ithm (CCGA) in FPGA. The CCGA and BBNN have cellular -like and ar r ay-like str uctur es which ar e suitable for har dwar e implementation. The implemented har dwar e demonstr ates the completely intr insic online evolution in har dwar e without softwar e r unning on micr opr ocessor s. This wor k contr ibutes to the field of evolvable har dwar e by pr oposing CCGA and a layer -based ar chitectur e to an integr ation of BBNN and CCGA as a kind of evolvable har dwar e.In addition, the pr oposed CCGA efficiently solves the scalable issues by scaling up to the size of BBNN. The pr esented appr oach demonstr ates a new kind of evolvable har dwar e.

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Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and Application

Cited by 16 publications

References 4 publications

Effective Linkage Learning Using Low-Order Statistics and Clustering

Effective Linkage Learning Using Low-Order Statistics and Clustering

Parallel BMDA with probability model migration

FPGA implementation of a cellular univariate estimation of distribution algorithm and block-based neural network as an evolvable hardware

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