Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training

Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to...
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Autor*in:	Bacanin, Nebojsa [verfasserIn] Bezdan, Timea Al-Turjman, Fadi Rashid, Tarik A.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Optimization Swarm intelligence Artificial flora algorithm Hybridized algorithms Artificial neural network Feature selection

Anmerkung:	© Taiwan Fuzzy Systems Association 2021

Übergeordnetes Werk:	Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 24(2021), 5 vom: 06. Nov., Seite 2538-2559
Übergeordnetes Werk:	volume:24 ; year:2021 ; number:5 ; day:06 ; month:11 ; pages:2538-2559

Links:	Volltext

DOI / URN:	10.1007/s40815-021-01191-x

Katalog-ID:	SPR047733845

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520			\|a Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection.
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allfields	10.1007/s40815-021-01191-x doi (DE-627)SPR047733845 (SPR)s40815-021-01191-x-e DE-627 ger DE-627 rakwb eng Bacanin, Nebojsa verfasserin (orcid)0000-0002-2062-924X aut Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Bezdan, Timea (orcid)0000-0001-6938-6974 aut Al-Turjman, Fadi (orcid)0000-0001-5418-873X aut Rashid, Tarik A. (orcid)0000-0002-8661-258X aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 24(2021), 5 vom: 06. Nov., Seite 2538-2559 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559 https://dx.doi.org/10.1007/s40815-021-01191-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 06 11 2538-2559
spelling	10.1007/s40815-021-01191-x doi (DE-627)SPR047733845 (SPR)s40815-021-01191-x-e DE-627 ger DE-627 rakwb eng Bacanin, Nebojsa verfasserin (orcid)0000-0002-2062-924X aut Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Bezdan, Timea (orcid)0000-0001-6938-6974 aut Al-Turjman, Fadi (orcid)0000-0001-5418-873X aut Rashid, Tarik A. (orcid)0000-0002-8661-258X aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 24(2021), 5 vom: 06. Nov., Seite 2538-2559 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559 https://dx.doi.org/10.1007/s40815-021-01191-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 06 11 2538-2559
allfields_unstemmed	10.1007/s40815-021-01191-x doi (DE-627)SPR047733845 (SPR)s40815-021-01191-x-e DE-627 ger DE-627 rakwb eng Bacanin, Nebojsa verfasserin (orcid)0000-0002-2062-924X aut Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Bezdan, Timea (orcid)0000-0001-6938-6974 aut Al-Turjman, Fadi (orcid)0000-0001-5418-873X aut Rashid, Tarik A. (orcid)0000-0002-8661-258X aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 24(2021), 5 vom: 06. Nov., Seite 2538-2559 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559 https://dx.doi.org/10.1007/s40815-021-01191-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 06 11 2538-2559
allfieldsGer	10.1007/s40815-021-01191-x doi (DE-627)SPR047733845 (SPR)s40815-021-01191-x-e DE-627 ger DE-627 rakwb eng Bacanin, Nebojsa verfasserin (orcid)0000-0002-2062-924X aut Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Bezdan, Timea (orcid)0000-0001-6938-6974 aut Al-Turjman, Fadi (orcid)0000-0001-5418-873X aut Rashid, Tarik A. (orcid)0000-0002-8661-258X aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 24(2021), 5 vom: 06. Nov., Seite 2538-2559 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559 https://dx.doi.org/10.1007/s40815-021-01191-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 06 11 2538-2559
allfieldsSound	10.1007/s40815-021-01191-x doi (DE-627)SPR047733845 (SPR)s40815-021-01191-x-e DE-627 ger DE-627 rakwb eng Bacanin, Nebojsa verfasserin (orcid)0000-0002-2062-924X aut Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2021 Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Bezdan, Timea (orcid)0000-0001-6938-6974 aut Al-Turjman, Fadi (orcid)0000-0001-5418-873X aut Rashid, Tarik A. (orcid)0000-0002-8661-258X aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 24(2021), 5 vom: 06. Nov., Seite 2538-2559 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559 https://dx.doi.org/10.1007/s40815-021-01191-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 06 11 2538-2559
language	English
source	Enthalten in International journal of fuzzy systems 24(2021), 5 vom: 06. Nov., Seite 2538-2559 volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559
sourceStr	Enthalten in International journal of fuzzy systems 24(2021), 5 vom: 06. Nov., Seite 2538-2559 volume:24 year:2021 number:5 day:06 month:11 pages:2538-2559
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Optimization Swarm intelligence Artificial flora algorithm Hybridized algorithms Artificial neural network Feature selection
isfreeaccess_bool	false
container_title	International journal of fuzzy systems
authorswithroles_txt_mv	Bacanin, Nebojsa @@aut@@ Bezdan, Timea @@aut@@ Al-Turjman, Fadi @@aut@@ Rashid, Tarik A. @@aut@@
publishDateDaySort_date	2021-11-06T00:00:00Z
hierarchy_top_id	612134636
id	SPR047733845
language_de	englisch
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author	Bacanin, Nebojsa
spellingShingle	Bacanin, Nebojsa misc Optimization misc Swarm intelligence misc Artificial flora algorithm misc Hybridized algorithms misc Artificial neural network misc Feature selection Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training
authorStr	Bacanin, Nebojsa
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topic_title	Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training Optimization (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Artificial flora algorithm (dpeaa)DE-He213 Hybridized algorithms (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213
topic	misc Optimization misc Swarm intelligence misc Artificial flora algorithm misc Hybridized algorithms misc Artificial neural network misc Feature selection
topic_unstemmed	misc Optimization misc Swarm intelligence misc Artificial flora algorithm misc Hybridized algorithms misc Artificial neural network misc Feature selection
topic_browse	misc Optimization misc Swarm intelligence misc Artificial flora algorithm misc Hybridized algorithms misc Artificial neural network misc Feature selection
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training
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title_full	Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training
author_sort	Bacanin, Nebojsa
journal	International journal of fuzzy systems
journalStr	International journal of fuzzy systems
lang_code	eng
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author_browse	Bacanin, Nebojsa Bezdan, Timea Al-Turjman, Fadi Rashid, Tarik A.
container_volume	24
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author-letter	Bacanin, Nebojsa
doi_str_mv	10.1007/s40815-021-01191-x
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title_sort	artificial flora optimization algorithm with genetically guided operators for feature selection and neural network training
title_auth	Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training
abstract	Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. © Taiwan Fuzzy Systems Association 2021
abstractGer	Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. © Taiwan Fuzzy Systems Association 2021
abstract_unstemmed	Abstract Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. © Taiwan Fuzzy Systems Association 2021
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title_short	Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training
url	https://dx.doi.org/10.1007/s40815-021-01191-x
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author2	Bezdan, Timea Al-Turjman, Fadi Rashid, Tarik A.
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doi_str	10.1007/s40815-021-01191-x
up_date	2024-07-03T14:38:10.082Z
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In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Swarm intelligence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial flora algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybridized algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bezdan, Timea</subfield><subfield code="0">(orcid)0000-0001-6938-6974</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Turjman, Fadi</subfield><subfield code="0">(orcid)0000-0001-5418-873X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rashid, Tarik A.</subfield><subfield code="0">(orcid)0000-0002-8661-258X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of fuzzy systems</subfield><subfield code="d">Taibei : Association, 2006</subfield><subfield code="g">24(2021), 5 vom: 06. Nov., Seite 2538-2559</subfield><subfield code="w">(DE-627)612134636</subfield><subfield code="w">(DE-600)2523322-1</subfield><subfield code="x">2199-3211</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:5</subfield><subfield code="g">day:06</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:2538-2559</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s40815-021-01191-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training

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