Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm

A Pareto genetic algorithm for automated biological sequence description and recognition was presented. The methods for the description of features lying the effective frontier or the set of all Pareto-optimal solutions of a multiobjective problem were discussed. An approach based on elimination, si...

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Detalles Bibliográficos
Autores principales: Zwir, I., Romero Zaliz, R.
Formato: CONF
Materias:
Biological Sequences
Fuzzy Logic
Generalized Clustering
Genetic Algorithms
Pareto Optimality
Pattern Recognition
Automation
Computational methods
Data mining
DNA
Fuzzy sets
Genetic algorithms
Identification (control systems)
Optimization
Pareto principle
Problem solving
Societies and institutions
Biological sequences
Generalized clustering
Pareto optimality
Pattern recognition
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_09707890_v6_n_p586_Zwir
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_09707890_v6_n_p586_Zwir
record_format dspace
spelling todo:paper_09707890_v6_n_p586_Zwir2023-10-03T15:55:33Z Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm Zwir, I. Romero Zaliz, R. Biological Sequences Fuzzy Logic Generalized Clustering Genetic Algorithms Pareto Optimality Pattern Recognition Automation Computational methods Data mining DNA Fuzzy sets Genetic algorithms Identification (control systems) Optimization Pareto principle Problem solving Societies and institutions Biological sequences Generalized clustering Pareto optimality Pattern recognition A Pareto genetic algorithm for automated biological sequence description and recognition was presented. The methods for the description of features lying the effective frontier or the set of all Pareto-optimal solutions of a multiobjective problem were discussed. An approach based on elimination, simplification and clustering techniques to summarize and organize the Pareto-optimal solutions was introduced. The experimental results of the application of the methodology to the description of DNA sequences were also presented. Fil:Romero Zaliz, R. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_09707890_v6_n_p586_Zwir
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Biological Sequences
Fuzzy Logic
Generalized Clustering
Genetic Algorithms
Pareto Optimality
Pattern Recognition
Automation
Computational methods
Data mining
DNA
Fuzzy sets
Genetic algorithms
Identification (control systems)
Optimization
Pareto principle
Problem solving
Societies and institutions
Biological sequences
Generalized clustering
Pareto optimality
Pattern recognition
spellingShingle Biological Sequences
Fuzzy Logic
Generalized Clustering
Genetic Algorithms
Pareto Optimality
Pattern Recognition
Automation
Computational methods
Data mining
DNA
Fuzzy sets
Genetic algorithms
Identification (control systems)
Optimization
Pareto principle
Problem solving
Societies and institutions
Biological sequences
Generalized clustering
Pareto optimality
Pattern recognition
Zwir, I.
Romero Zaliz, R.
Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
topic_facet Biological Sequences
Fuzzy Logic
Generalized Clustering
Genetic Algorithms
Pareto Optimality
Pattern Recognition
Automation
Computational methods
Data mining
DNA
Fuzzy sets
Genetic algorithms
Identification (control systems)
Optimization
Pareto principle
Problem solving
Societies and institutions
Biological sequences
Generalized clustering
Pareto optimality
Pattern recognition
description A Pareto genetic algorithm for automated biological sequence description and recognition was presented. The methods for the description of features lying the effective frontier or the set of all Pareto-optimal solutions of a multiobjective problem were discussed. An approach based on elimination, simplification and clustering techniques to summarize and organize the Pareto-optimal solutions was introduced. The experimental results of the application of the methodology to the description of DNA sequences were also presented.
format CONF
author Zwir, I.
Romero Zaliz, R.
author_facet Zwir, I.
Romero Zaliz, R.
author_sort Zwir, I.
title Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
title_short Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
title_full Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
title_fullStr Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
title_full_unstemmed Automated Biological Sequence description and recognition by a localized multiobjective genetic algorithm
title_sort automated biological sequence description and recognition by a localized multiobjective genetic algorithm
url http://hdl.handle.net/20.500.12110/paper_09707890_v6_n_p586_Zwir
work_keys_str_mv AT zwiri automatedbiologicalsequencedescriptionandrecognitionbyalocalizedmultiobjectivegeneticalgorithm
AT romerozalizr automatedbiologicalsequencedescriptionandrecognitionbyalocalizedmultiobjectivegeneticalgorithm
_version_ 1807320662556016640

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