Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/7094
Full metadata record
DC FieldValueLanguage
dc.contributor.authorD'Souza, R.G.L.-
dc.contributor.authorChandra, Sekaran, K.-
dc.contributor.authorKandasamy, A.-
dc.date.accessioned2020-03-30T09:58:29Z-
dc.date.available2020-03-30T09:58:29Z-
dc.date.issued2010-
dc.identifier.citationCommunications in Computer and Information Science, 2010, Vol.94 CCIS, PART 1, pp.194-205en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7094-
dc.description.abstractGenetic algorithms require a fitness function to evaluate individuals in a population. The fitness function essentially captures the dependence of the phenotype on the genotype. In the Phenomic approach we represent the phenotype of each individual in a simulated environment where phenotypic interactions are enforced. In reconstruction type of problems, the model is reconstructed from the data that maps the input to the output. In the phenomic algorithm, we use this data to replace the fitness function. Thus we achieve survival-of-the- fittest without the need for a fitness function. Though limited to reconstruction type problems where such mapping data is available, this novel approach nonetheless overcomes the daunting task of providing the elusive fitness function, which has been a stumbling block so far to the widespread use of genetic algorithms. We present an algorithm called Integrated Pheneto-Genetic Algorithm (IPGA), wherein the genetic algorithm is used to process genotypic information and the phenomic algorithm is used to process phenotypic information, thereby providing a holistic approach which completes the evolutionary cycle. We apply this novel evolutionary algorithm to the problem of elucidation of gene networks from microarray data. The algorithm performs well and provides stable and accurate results when compared to some other existing algorithms. � 2010 Springer-Verlag Berlin Heidelberg.en_US
dc.titleA phenomic approach to genetic algorithms for reconstruction of gene networksen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
7094.pdf182.82 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.