Gene level concurrency in genetic algorithms

Date

2003-01-01

Author

Şehitoğlu, Onur Tolga
Üçoluk, Göktürk

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

189
views

0
downloads

This study describes an alternative concurrency approach in genetic algorithms. Inspiring from implicit parallelism in a physical chromosome, a vertical concurrency is introduced. Proposed gene process model allows genetic algorithms work in encodings independent from the gene position ordering in a chromosome. This feature is used to implement a gene reordering version of genetic algorithm. Further possible models of flexible gene position encodings are discussed.

URI

https://hdl.handle.net/11511/55878

Journal

COMPUTER AND INFORMATION SCIENCES - ISCIS 2003

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Gene Level Concurrency in Genetic Algorithms Şehitoğlu, Onur Tolga; Üçoluk, Göktürk (Springer-Verlag, 2007-01-01) This study describes an alternative concurrency approach in genetic algorithms. Inspiring from implicit parallelism in a physical chromosome, a vertical concurrency is introduced. Proposed gene process model allows genetic algorithms work in encodings independent from the gene position ordering in a chromosome. This feature is used to implement a gene reordering version of genetic algorithm. Further possible models of flexible gene position encodings are discussed.
Gene reordering and concurrency in genetic algorithms Şehitoğlu, Onur Tolga; Üçoluk, Göktürk; Department of Computer Engineering (2002) This study first introduces an order-free chromosome encoding to enhance the performance of genetic algorithms by learning the linkage of building blocks in non-binary encodings. The method introduces a measure called affinity which is based on the statistical properties of gene valuations in the population. It uses the affinity values of the local and global gene pairs to construct a global permutation with tight building block positioning. Method is tested and experimental results are shown for a group of...
Interval priority weight generation from interval comparison matrices in analytic hierarchy process Öztürk, Ufuk; Karasakal, Esra; Department of Industrial Engineering (2009) In this study, for the well-known Analytic Hierarchy Process (AHP) method a new approach to interval priority weight generation from interval comparison matrix is proposed. This method can be used for both inconsistent and consistent matrices. Also for the problems having more than two hierarchical levels a synthesizing heuristic is presented. The performances of the methods, interval generation and synthesizing, are compared with the methods that are already available in the literature on randomly generate...
Direction of arrival estimation algorithm with uniform linear and circular array Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Guelbin (2007-01-01) In this paper mutual coupling effects on Modified Neural Multiple Source Tracking Algorithm (MN-MUST) has been studied. MN-MUST algorithm applied to the Uniform Circular Array (UCA) geometry for the first time. The validity of MN-MUST algorithm in the presence of mutual coupling has been proved for both Uniform Linear Array (ULA) and UCA. Simulation results of MN-MUST algorithm are provided for UCA for the first time. The presence of mutual coupling degraded the MN-MUST algorithm performed in the absence of...
Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification DAĞ, OSMAN; KAŞIKCI, MERVE; KARABULUT, ERDEM; Alpar, Reha (Informa UK Limited, 2019-12-03) Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate ...

Citation Formats

O. T. Şehitoğlu and G. Üçoluk, “Gene level concurrency in genetic algorithms,” COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, pp. 976–983, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55878.