Developing a Distributed Self Adaptive Genetic Algorithm with Migration for Identification of Electrical Customer Patterns
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
1 - Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
الکلمات المفتاحية: Data visualization, Adaptive genetic algorithms, Quadratic assignment problem and Clustering,
ملخص المقالة :
Data visualization is a key component of undirected data mining that it transforms data, information, and knowledge into visual view. In this paper, we formulate data visualization problem as a quadratic assignment problem (DV-QAP). The QAP is an NP-Hard problem and has high complexity that it is more acute for data visualization problem because it has intense dependencies among variables and big search space. Therefore, the exact approaches are inefficient to solve DV-QAP and we introduce a new technique called Distributed Self Adaptive Genetic Algorithm with Migration (DSAGAM) that their parameters adjust to increases the exploration and exploitation. This paper focuses on the effect of controlling the migration process and adjusting parameters with respect to the fitness to explore such big search spaces to improve solutions quality. Then we demonstrate the efficiency of the model for a real data set compared with the SGA, SAMGA, IGA and Sammon's mapping approaches.
المصادر:
1 Li, Q., Liu, Y., Zhang, Z., Liu, J., Yuan, Y., Wang, K., and He, R.: ‘Learning with incomplete labels of multisource datasets for ECG classification’, Pattern Recognition, 2024, 150, pp. 110321
2 Liu, C., Cui, Y., Li, X., and Yao, M.: ‘microeco: an R package for data mining in microbial community ecology’, FEMS microbiology ecology, 2021, 97, (2), pp. fiaa255
3 Alves, C.L., and Rodrigues, F.A.: ‘Diagnosis of neurological diseases based on data mining and complex network’, Anais, 2019
4 Sun, X., Li, B., Sutcliffe, R., Gao, Z., Kang, W., and Feng, J.: ‘Wse-MF: A weighting-based student exercise matrix factorization model’, Pattern Recognition, 2023, 138, pp. 109285
5 Penteado, B.E., Paiva, P.M.P., Zupelari, M.M., Lopes, N.B.F., Ferrari, D.V., and Isotani, S.: ‘Educational data mining methods: opportunities for teaching and learning in Audiology’, in Editor (Ed.)^(Eds.): ‘Book Educational data mining methods: opportunities for teaching and learning in Audiology’ (2017, edn.), pp.
6 Paiva, P.M.P., Penteado, B., Zupelari, M.M., Lopes, N.B.F., Costa Filho, O.A., Isotani, S., and Ferrari, D.V.: ‘Educational data mining for understanding students' behavior in a virtual learning environments: a case study’, in Editor (Ed.)^(Eds.): ‘Book Educational data mining for understanding students' behavior in a virtual learning environments: a case study’ (2017, edn.), pp.
7 Cano, A., and Krawczyk, B.: ‘Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams’, Pattern Recognition, 2019, 87, pp. 248-268
8 Dajer, M.E., Tsutsumi, M., Pimienta, R.A., de Olivera, V.H.C., Isotani, S., Delbem, A.C.B., Hachiya, A., and Tsuji, D.H.: ‘Preliminary study of Data Mining analysis of high speed Kymography and voice data’, Areté, 2018, 18, (2), pp. 11-20
9 De Nadai Fernandes, E.A., Tagliaferro, F.S., Azevedo-Filho, A., and Bode, P.: ‘Organic coffee discrimination with INAA and data mining/KDD techniques: new perspectives for coffee trade’, Accreditation and quality assurance, 2002, 7, pp. 378-387
10 Rodrigues, L., Rodrigues, L., and Bacchi, M.R.P.: ‘Performance evaluation of forecasting models based on time series and machine learning techniques: an application to light fuel consumption in Brazil’, International Journal of Energy Sector Management, 2022, 16, (4), pp. 636-658
11 Paletta, F.C., and Tavares, L.W.: ‘Complex network research data mining’, Anais, 2020
12 Mabu, A.M., Prasad, R., and Yadav, R.: ‘Mining gene expression data using data mining techniques: A critical review’, Journal of Information and Optimization Sciences, 2020, 41, (3), pp. 723-742
13 Mohd Selamat, S.A., Prakoonwit, S., Sahandi, R., Khan, W., and Ramachandran, M.: ‘Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector’, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8, (3), pp. e1238
14 Koukouvinis, P., Rodriguez, C., Hwang, J., Karathanassis, I., Gavaises, M., and Pickett, L.: ‘Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A’, International Journal of Engine Research, 2022, 23, (9), pp. 1556-1572
15 Niebel, T., Rasel, F., and Viete, S.: ‘BIG data–BIG gains? Understanding the link between big data analytics and innovation’, Economics of Innovation and New Technology, 2019, 28, (3), pp. 296-316
16 Hasugian, P.M., and Nadeak, B.: ‘Bibliometric Analysis On Techniques For Data Visualization’, Jurnal Info Sains: Informatika dan Sains, 2024, 14, (01), pp. 425-433
17 Silva, R.B., Oliveira, D.d., Santos, D.P.d., Santos, L.F.D., Wilson, R.E., and Bêdo, M.V.N.: ‘Criteria for choosing the number of dimensions in a principal component analysis: An empirical assessment’, Anais, 2020
18 Mair, P., Groenen, P.J., and de Leeuw, J.: ‘More on multidimensional scaling and unfolding in R: smacof version 2’, Journal of Statistical Software, 2022, 102, pp. 1-47
19 Du, M., and Yuan, X.: ‘A survey of competitive sports data visualization and visual analysis’, Journal of Visualization, 2021, 24, pp. 47-67
20 Stryhal, J., and Plavcová, E.: ‘On using self‐organizing maps and discretized Sammon maps to study links between atmospheric circulation and weather extremes’, International Journal of Climatology, 2023, 43, (6), pp. 2678-2698
21 Nanga, S., Bawah, A.T., Acquaye, B.A., Billa, M.-I., Baeta, F.D., Odai, N.A., Obeng, S.K., and Nsiah, A.D.: ‘Review of dimension reduction methods’, Journal of Data Analysis and Information Processing, 2021, 9, (3), pp. 189-231
22 Koopmans, T., and Beckmann, M.: ‘Assignment problems and the location of economic activities’, Econometrica, 1957, 25, pp. 53–76
23 Verel, S., Thomson, S., and Rifki, O.: ‘Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances’, arXiv preprint arXiv:2403.02783, 2024
24 Ande, A., Subhash, V., and Natarajan, V.: ‘tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs’, in Editor (Ed.)^(Eds.): ‘Book tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs’ (Wiley Online Library, 2023, edn.), pp. e14784
25 Shao, X., Dai, X., and He, X.: ‘Noise robustness and parallel computation of the inverse compositional Gauss–Newton algorithm in digital image correlation’, Optics and Lasers in Engineering, 2015, 71, pp. 9-19
26 Yan, B., and Regueiro, R.A.: ‘Three‐dimensional discrete element method parallel computation of Cauchy stress distribution over granular materials’, International Journal for Numerical and Analytical Methods in Geomechanics, 2019, 43, (5), pp. 974-1004
27 Zhu, Z.-j., Ma, S.-q., Zhu, X.-Q., Lan, Q., Piao, S.-C., and Cheng, Y.-S.: ‘Parallel optimization of underwater acoustic models: A survey’, Chinese Physics B, 2022, 31, (10), pp. 104301
28 Zhong, M., Yue, Y., Zhou, L., and Zhu, J.: ‘Parallel optimization method of train scheduling and shunting at complex high‐speed railway stations’, Computer‐Aided Civil and Infrastructure Engineering, 2024, 39, (5), pp. 731-755
29 Xue, X., Yang, C., Mao, G., and Zhu, H.: ‘Semi-automatic ontology matching based on interactive compact genetic algorithm’, International Journal of Pattern Recognition and Artificial Intelligence, 2022, 36, (05), pp. 2257002
30 Li, Y., Wu, L., Wang, T., Gao, N., and Wang, Q.: ‘EEG signal processing based on genetic algorithm for extracting mixed features’, International Journal of Pattern Recognition and Artificial Intelligence, 2019, 33, (06), pp. 1958008
31 Zubel, M., Ottenwälder, T., Heuser, B., and Pischinger, S.: ‘Combustion system optimization for dimethyl ether using a genetic algorithm’, International Journal of Engine Research, 2021, 22, (1), pp. 22-38
32 Ren, Y., Tang, J., Yu, Y., and Li, X.: ‘A two-stage stochastic programming model and parallel Master–Slave adaptive GA for flexible Seru system formation’, International Journal of Production Research, 2024, 62, (4), pp. 1144-1161
33 Cui, Z., Zhao, B., Zhao, T., Cai, X., and Chen, J.: ‘An adaptive differential evolution algorithm based on archive reuse’, Information Sciences, 2024, pp. 120524
34 Mishra, R., and Bajpai, M.K.: ‘A priority-based self-guided serial-parallel genetic algorithm for low-dose computed tomography’, Applied Soft Computing, 2024, pp. 111520
35 Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., and Zhao, H.: ‘An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems’, Information Sciences, 2022, 585, pp. 441-453
36 Rahimi, I., Gandomi, A.H., Deb, K., Chen, F., and Nikoo, M.R.: ‘Scheduling by NSGA-II: Review and bibliometric analysis’, Processes, 2022, 10, (1), pp. 98
37 Teodosio, L., and Berni, F.: ‘Optimization via genetic algorithm of a variable-valve-actuation spark-ignition engine based on the integration between 1D/3D simulation codes and optimizer’, International Journal of Engine Research, 2023, 24, (4), pp. 1760-1784
38 Dushatskiy, A., Virgolin, M., Bouter, A., Thierens, D., and Bosman, P.A.: ‘Parameterless gene-pool optimal mixing evolutionary algorithms’, Evolutionary Computation, 2023, pp. 1-28
39 Przewozniczek, M.W., Dziurzanski, P., Zhao, S., and Indrusiak, L.S.: ‘Multi-Objective parameter-less population pyramid for solving industrial process planning problems’, Swarm and Evolutionary Computation, 2021, 60, pp. 100773
40 Aygun, H., and Turan, O.: ‘Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase’, Energy, 2022, 238, pp. 121644
41 Back, T.: ‘Self-adaptation in genetic algorithms’, in Editor (Ed.)^(Eds.): ‘Book Self-adaptation in genetic algorithms’ (1992, edn.), pp. 263-271
42 Krink, T., and Ursem, R.K.: ‘Parameter control using the agent based patchwork model’, in Editor (Ed.)^(Eds.): ‘Book Parameter control using the agent based patchwork model’ (2000, edn.), pp.
43 Murata, Y., Shibata, N., Yasumoto, K., and Ito, M.: ‘Agent oriented self adaptive genetic algorithm’, Proceedings of Communications and Computer Networks (CCN), 2002, pp. 348-353
44 Weinberg, R., and Berkus, M.: ‘Computer simulation of a living cell: Part I’, International Journal of Bio-Medical Computing, 1971, 2, (2), pp. 95-120
45 Hiroyasu, T., Kaneko, M., and Hatanaka, K.: ‘A parallel genetic algorithm with distributed environment scheme’, in Editor (Ed.)^(Eds.): ‘Book A parallel genetic algorithm with distributed environment scheme’ (Citeseer, 1999, edn.), pp. 695-700
46 Takashima, E., Murata, Y., Shibata, N., and Ito, M.: ‘Asynchronous Implemention of Self Adaptive Island Genetic Algorithm’, Joho Shori Gakkai Kenkyu Hokoku, 2003, 2003, (91), pp. 65-68
47 Takashima, E., Murata, Y., Shibata, N., and Ito, M.: ‘Techniques to improve exploration efficiency of parallel self adaptive genetic algorithms by dispensing synchronization’, in Editor (Ed.)^(Eds.): ‘Book Techniques to improve exploration efficiency of parallel self adaptive genetic algorithms by dispensing synchronization’ (Citeseer, 2004, edn.), pp.
48 Srinivasa, K.G., Venugopal, K.R., and Patnaik, L.M.: ‘A self-adaptive migration model genetic algorithm for data mining applications’, Information Sciences, 2007, 177, (20), pp. 4295-4313
49 Chien-Pang Lee, W.-S.L., Yuh-Min Chen, Bo-Jein Kuo: ‘Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method’, Expert Systems with Applications, 2011, 38, pp. 4661–4667
50 Abbiw-Jackson, R., Golden, B., Raghavan, S., and Wasil, E.: ‘A divide-and-conquer local search heuristic for data visualization’, Computers and operations research, 2006, 33, (11), pp. 3070-3087
51 Widayanti, R., Madenda, S., Wibowo, E.P., and Anwar, K.: ‘SOM-SIS approach to auto summary of clustering results on university academic performance’, TELKOMNIKA (Telecommunication Computing Electronics and Control), 2023, 21, (1), pp. 104-112
52 Yang Xu, Lu Xu, and W.S.Chow, T.: ‘PPoSOM: A new variant of PolSOM by using probabilistic assignmen tfor multi dimensional data visualization’, Neuro computing, 2011, 74, pp. 2018–2027
1 Li, Q., Liu, Y., Zhang, Z., Liu, J., Yuan, Y., Wang, K., and He, R.: ‘Learning with incomplete labels of multisource datasets for ECG classification’, Pattern Recognition, 2024, 150, pp. 110321
2 Liu, C., Cui, Y., Li, X., and Yao, M.: ‘microeco: an R package for data mining in microbial community ecology’, FEMS microbiology ecology, 2021, 97, (2), pp. fiaa255
3 Alves, C.L., and Rodrigues, F.A.: ‘Diagnosis of neurological diseases based on data mining and complex network’, Anais, 2019
4 Sun, X., Li, B., Sutcliffe, R., Gao, Z., Kang, W., and Feng, J.: ‘Wse-MF: A weighting-based student exercise matrix factorization model’, Pattern Recognition, 2023, 138, pp. 109285
5 Penteado, B.E., Paiva, P.M.P., Zupelari, M.M., Lopes, N.B.F., Ferrari, D.V., and Isotani, S.: ‘Educational data mining methods: opportunities for teaching and learning in Audiology’, in Editor (Ed.)^(Eds.): ‘Book Educational data mining methods: opportunities for teaching and learning in Audiology’ (2017, edn.), pp.
6 Paiva, P.M.P., Penteado, B., Zupelari, M.M., Lopes, N.B.F., Costa Filho, O.A., Isotani, S., and Ferrari, D.V.: ‘Educational data mining for understanding students' behavior in a virtual learning environments: a case study’, in Editor (Ed.)^(Eds.): ‘Book Educational data mining for understanding students' behavior in a virtual learning environments: a case study’ (2017, edn.), pp.
7 Cano, A., and Krawczyk, B.: ‘Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams’, Pattern Recognition, 2019, 87, pp. 248-268
8 Dajer, M.E., Tsutsumi, M., Pimienta, R.A., de Olivera, V.H.C., Isotani, S., Delbem, A.C.B., Hachiya, A., and Tsuji, D.H.: ‘Preliminary study of Data Mining analysis of high speed Kymography and voice data’, Areté, 2018, 18, (2), pp. 11-20
9 De Nadai Fernandes, E.A., Tagliaferro, F.S., Azevedo-Filho, A., and Bode, P.: ‘Organic coffee discrimination with INAA and data mining/KDD techniques: new perspectives for coffee trade’, Accreditation and quality assurance, 2002, 7, pp. 378-387
10 Rodrigues, L., Rodrigues, L., and Bacchi, M.R.P.: ‘Performance evaluation of forecasting models based on time series and machine learning techniques: an application to light fuel consumption in Brazil’, International Journal of Energy Sector Management, 2022, 16, (4), pp. 636-658
11 Paletta, F.C., and Tavares, L.W.: ‘Complex network research data mining’, Anais, 2020
12 Mabu, A.M., Prasad, R., and Yadav, R.: ‘Mining gene expression data using data mining techniques: A critical review’, Journal of Information and Optimization Sciences, 2020, 41, (3), pp. 723-742
13 Mohd Selamat, S.A., Prakoonwit, S., Sahandi, R., Khan, W., and Ramachandran, M.: ‘Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector’, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8, (3), pp. e1238
14 Koukouvinis, P., Rodriguez, C., Hwang, J., Karathanassis, I., Gavaises, M., and Pickett, L.: ‘Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A’, International Journal of Engine Research, 2022, 23, (9), pp. 1556-1572
15 Niebel, T., Rasel, F., and Viete, S.: ‘BIG data–BIG gains? Understanding the link between big data analytics and innovation’, Economics of Innovation and New Technology, 2019, 28, (3), pp. 296-316
16 Hasugian, P.M., and Nadeak, B.: ‘Bibliometric Analysis On Techniques For Data Visualization’, Jurnal Info Sains: Informatika dan Sains, 2024, 14, (01), pp. 425-433
17 Silva, R.B., Oliveira, D.d., Santos, D.P.d., Santos, L.F.D., Wilson, R.E., and Bêdo, M.V.N.: ‘Criteria for choosing the number of dimensions in a principal component analysis: An empirical assessment’, Anais, 2020
18 Mair, P., Groenen, P.J., and de Leeuw, J.: ‘More on multidimensional scaling and unfolding in R: smacof version 2’, Journal of Statistical Software, 2022, 102, pp. 1-47
19 Du, M., and Yuan, X.: ‘A survey of competitive sports data visualization and visual analysis’, Journal of Visualization, 2021, 24, pp. 47-67
20 Stryhal, J., and Plavcová, E.: ‘On using self‐organizing maps and discretized Sammon maps to study links between atmospheric circulation and weather extremes’, International Journal of Climatology, 2023, 43, (6), pp. 2678-2698
21 Nanga, S., Bawah, A.T., Acquaye, B.A., Billa, M.-I., Baeta, F.D., Odai, N.A., Obeng, S.K., and Nsiah, A.D.: ‘Review of dimension reduction methods’, Journal of Data Analysis and Information Processing, 2021, 9, (3), pp. 189-231
22 Koopmans, T., and Beckmann, M.: ‘Assignment problems and the location of economic activities’, Econometrica, 1957, 25, pp. 53–76
23 Verel, S., Thomson, S., and Rifki, O.: ‘Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances’, arXiv preprint arXiv:2403.02783, 2024
24 Ande, A., Subhash, V., and Natarajan, V.: ‘tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs’, in Editor (Ed.)^(Eds.): ‘Book tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs’ (Wiley Online Library, 2023, edn.), pp. e14784
25 Shao, X., Dai, X., and He, X.: ‘Noise robustness and parallel computation of the inverse compositional Gauss–Newton algorithm in digital image correlation’, Optics and Lasers in Engineering, 2015, 71, pp. 9-19
26 Yan, B., and Regueiro, R.A.: ‘Three‐dimensional discrete element method parallel computation of Cauchy stress distribution over granular materials’, International Journal for Numerical and Analytical Methods in Geomechanics, 2019, 43, (5), pp. 974-1004
27 Zhu, Z.-j., Ma, S.-q., Zhu, X.-Q., Lan, Q., Piao, S.-C., and Cheng, Y.-S.: ‘Parallel optimization of underwater acoustic models: A survey’, Chinese Physics B, 2022, 31, (10), pp. 104301
28 Zhong, M., Yue, Y., Zhou, L., and Zhu, J.: ‘Parallel optimization method of train scheduling and shunting at complex high‐speed railway stations’, Computer‐Aided Civil and Infrastructure Engineering, 2024, 39, (5), pp. 731-755
29 Xue, X., Yang, C., Mao, G., and Zhu, H.: ‘Semi-automatic ontology matching based on interactive compact genetic algorithm’, International Journal of Pattern Recognition and Artificial Intelligence, 2022, 36, (05), pp. 2257002
30 Li, Y., Wu, L., Wang, T., Gao, N., and Wang, Q.: ‘EEG signal processing based on genetic algorithm for extracting mixed features’, International Journal of Pattern Recognition and Artificial Intelligence, 2019, 33, (06), pp. 1958008
31 Zubel, M., Ottenwälder, T., Heuser, B., and Pischinger, S.: ‘Combustion system optimization for dimethyl ether using a genetic algorithm’, International Journal of Engine Research, 2021, 22, (1), pp. 22-38
32 Ren, Y., Tang, J., Yu, Y., and Li, X.: ‘A two-stage stochastic programming model and parallel Master–Slave adaptive GA for flexible Seru system formation’, International Journal of Production Research, 2024, 62, (4), pp. 1144-1161
33 Cui, Z., Zhao, B., Zhao, T., Cai, X., and Chen, J.: ‘An adaptive differential evolution algorithm based on archive reuse’, Information Sciences, 2024, pp. 120524
34 Mishra, R., and Bajpai, M.K.: ‘A priority-based self-guided serial-parallel genetic algorithm for low-dose computed tomography’, Applied Soft Computing, 2024, pp. 111520
35 Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., and Zhao, H.: ‘An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems’, Information Sciences, 2022, 585, pp. 441-453
36 Rahimi, I., Gandomi, A.H., Deb, K., Chen, F., and Nikoo, M.R.: ‘Scheduling by NSGA-II: Review and bibliometric analysis’, Processes, 2022, 10, (1), pp. 98
37 Teodosio, L., and Berni, F.: ‘Optimization via genetic algorithm of a variable-valve-actuation spark-ignition engine based on the integration between 1D/3D simulation codes and optimizer’, International Journal of Engine Research, 2023, 24, (4), pp. 1760-1784
38 Dushatskiy, A., Virgolin, M., Bouter, A., Thierens, D., and Bosman, P.A.: ‘Parameterless gene-pool optimal mixing evolutionary algorithms’, Evolutionary Computation, 2023, pp. 1-28
39 Przewozniczek, M.W., Dziurzanski, P., Zhao, S., and Indrusiak, L.S.: ‘Multi-Objective parameter-less population pyramid for solving industrial process planning problems’, Swarm and Evolutionary Computation, 2021, 60, pp. 100773
40 Aygun, H., and Turan, O.: ‘Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase’, Energy, 2022, 238, pp. 121644
41 Back, T.: ‘Self-adaptation in genetic algorithms’, in Editor (Ed.)^(Eds.): ‘Book Self-adaptation in genetic algorithms’ (1992, edn.), pp. 263-271
42 Krink, T., and Ursem, R.K.: ‘Parameter control using the agent based patchwork model’, in Editor (Ed.)^(Eds.): ‘Book Parameter control using the agent based patchwork model’ (2000, edn.), pp.
43 Murata, Y., Shibata, N., Yasumoto, K., and Ito, M.: ‘Agent oriented self adaptive genetic algorithm’, Proceedings of Communications and Computer Networks (CCN), 2002, pp. 348-353
44 Weinberg, R., and Berkus, M.: ‘Computer simulation of a living cell: Part I’, International Journal of Bio-Medical Computing, 1971, 2, (2), pp. 95-120
45 Hiroyasu, T., Kaneko, M., and Hatanaka, K.: ‘A parallel genetic algorithm with distributed environment scheme’, in Editor (Ed.)^(Eds.): ‘Book A parallel genetic algorithm with distributed environment scheme’ (Citeseer, 1999, edn.), pp. 695-700
46 Takashima, E., Murata, Y., Shibata, N., and Ito, M.: ‘Asynchronous Implemention of Self Adaptive Island Genetic Algorithm’, Joho Shori Gakkai Kenkyu Hokoku, 2003, 2003, (91), pp. 65-68
47 Takashima, E., Murata, Y., Shibata, N., and Ito, M.: ‘Techniques to improve exploration efficiency of parallel self adaptive genetic algorithms by dispensing synchronization’, in Editor (Ed.)^(Eds.): ‘Book Techniques to improve exploration efficiency of parallel self adaptive genetic algorithms by dispensing synchronization’ (Citeseer, 2004, edn.), pp.
48 Srinivasa, K.G., Venugopal, K.R., and Patnaik, L.M.: ‘A self-adaptive migration model genetic algorithm for data mining applications’, Information Sciences, 2007, 177, (20), pp. 4295-4313
49 Chien-Pang Lee, W.-S.L., Yuh-Min Chen, Bo-Jein Kuo: ‘Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method’, Expert Systems with Applications, 2011, 38, pp. 4661–4667
50 Abbiw-Jackson, R., Golden, B., Raghavan, S., and Wasil, E.: ‘A divide-and-conquer local search heuristic for data visualization’, Computers and operations research, 2006, 33, (11), pp. 3070-3087
51 Widayanti, R., Madenda, S., Wibowo, E.P., and Anwar, K.: ‘SOM-SIS approach to auto summary of clustering results on university academic performance’, TELKOMNIKA (Telecommunication Computing Electronics and Control), 2023, 21, (1), pp. 104-112
52 Yang Xu, Lu Xu, and W.S.Chow, T.: ‘PPoSOM: A new variant of PolSOM by using probabilistic assignmen tfor multi dimensional data visualization’, Neuro computing, 2011, 74, pp. 2018–2027
