Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms

Goldbarg, Elizabeth Ferreira Gouvêa; Goldbarg, Marco César

doi:10.1007/978-3-642-01085-9_14

Cited by 12 publications

(5 citation statements)

References 47 publications

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“…Such research often involves elements of online ethnography, whereby researchers work with data from social networks they themselves belong to (as with Georgakopoulou 1997 andTsiplakou 2009), and focuses on functions rather than structures of CS. "Quantitative" encompasses quantifications based on questionnaire data (Goldbarg 2009;Tsiplakou 2009) or coding of textual data (as with Paolillo 2001;Siebenhaar 2008). What is remarkable is the frequency of mixed method approaches, which combine a "bird's eye view" of the distribution of languages over a large data set with a detailed view of local processes of switching and mixing.…”

Section: Code-switching Across Cmc Modesmentioning

confidence: 99%

27. Code-switching in computer-mediated communication

Androutsopoulos¹

2013

Pragmatics of Computer-Mediated Communication

141

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Section: Code-switching Across Cmc Modesmentioning

confidence: 99%

27. Code-switching in computer-mediated communication

Androutsopoulos¹

2013

Pragmatics of Computer-Mediated Communication

141

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“…The inspiration for the Transgenetic algorithms came from two major evolutionary forces: horizontal gene transfer and endosymbiosis [8]. The metaphor of these algorithms relies on a process that is thought to occur between a host cell and its endosymbionts.…”

Section: Transgenetic Algorithmmentioning

confidence: 99%

“…Transgenetic algorithms are evolutionary computing techniques based on living processes where cooperation is the main evolutionary strategy [8]. The successful application of this algorithm to the biobjective problem motivated the research for the tri-objective variation.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithms for a three-objectives oil derivatives network problem

Souza

Goldbarg

2013

2013 IEEE Congress on Evolutionary Computation

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To distribute oil derivatives by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when scheduling products in pipeline networks. Nevertheless, other costs may also be considered such as losses due to interfaces and electrical energy. Losses due to interfaces occur when different products sent consecutively contaminate each other. Usually, no separation devices exist between batches of different products and losses due to interface can be significant. The price paid for electrical energy varies during the day, so it is important also to try to minimize this cost. These three minimization objectives are considered, simultaneously, i.e. delivery time at demand nodes, interface losses and electrical energy cost. A transgenetic algorithm is proposed and applied to thirty random instances. The results obtained with the proposed method are compared with those produced by an NSGAII algorithm. Keywords-distribution network; oil derivatives; multi-objective evolutionary algorithmI.

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“…Desenvolvidos, inicialmente, no trabalho de Gouvêa (2001), foram aplicados a diversos problemas teóricos e práticos. Uma visão geral da técnica é apresentada no trabalho de Goldbarg & Goldbarg (2009), incluindo uma revisão do estado da arte de algoritmos baseados em abordagens correlatas.…”

Section: Algoritmo Transgenéticounclassified

Algoritmo evolucionário para otimização do plano de tratamento em radioterapia conformal 3D

Goldbarg

Mendes

et al. 2009

Pesqui. Oper.

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O planejamento do tratamento por radioterapia tem por objetivo atingir um volume alvo com altas doses de radiação tomando cuidado para não expor órgãos sadios a doses elevadas. É, portanto, muito importante que se encontre um balanço ideal entre esses objetivos conflitantes. O presente trabalho relata um modelo de programação matemática multiobjetivo e introduz um Algoritmo Transgenético para o problema de seleção do direcionamento dos feixes de radiação no planejamento em radioterapia conformal 3D. A seleção das direções dos feixes é feita através de uma técnica denominada de isocentros variáveis. Com a finalidade de testar o potencial do algoritmo desenvolvido, realiza-se um experimento comparativo com um Algoritmo Genético Multiobjetivo. O experimento computacional obtém dados quantitativos e qualitativos que são analisados no trabalho.
The radiotherapy treatment planning aims to achieve a target volume with high doses of radiation taking care not to expose healthy organs to high doses. It is therefore very important to find an optimal balance between these conflicting goals. This paper reports a mathematical model of multiobjective programming and presents a Transgenetic Algorithm for the problem of selecting the direction of radiation beams in 3D conformal radiotherapy planning. The selection of beams directions is done with a technique called variable isocenters. In order to test the potential of the developed algorithm, a comparative experiment with a multiobjective genetic algorithm was done. The computational experiment obtains quantitative and qualitative data that are analyzed in this paper

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Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms

Cited by 12 publications

References 47 publications

27. Code-switching in computer-mediated communication

27. Code-switching in computer-mediated communication

Evolutionary algorithms for a three-objectives oil derivatives network problem

Algoritmo evolucionário para otimização do plano de tratamento em radioterapia conformal 3D

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