A variant of genetic algorithm for non-homogeneous population
Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups cons...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2017-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/itmconf/20170902001 |
| _version_ | 1818351453104766976 |
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| author | Alibabaie Najmeh Ghasemzadeh Mohammad Meinel Christoph |
| author_facet | Alibabaie Najmeh Ghasemzadeh Mohammad Meinel Christoph |
| author_sort | Alibabaie Najmeh |
| collection | DOAJ |
| description | Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups consisting of elements with similar structure. Elements in a group have the same size, while elements in different groups are of different sizes. The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers. In the proposed method, the crossover and mutation operators can accept parents with different sizes; this can lead to versatility in population and information transfer among sub-populations. We implemented the proposed method and evaluated its performance against some random datasets and the Ruspini dataset as well. The experimental results show that the proposed method could effectively determine the appropriate number of clusters and recognize their centers. Overall this research implies that using heterogeneous population in the genetic algorithm can lead to better results. |
| first_indexed | 2024-12-13T18:37:59Z |
| format | Article |
| id | doaj.art-d14bcdbd3dd24fc78740d57712ea067e |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-13T18:37:59Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-d14bcdbd3dd24fc78740d57712ea067e2022-12-21T23:35:18ZengEDP SciencesITM Web of Conferences2271-20972017-01-0190200110.1051/itmconf/20170902001itmconf_amcse2017_02001A variant of genetic algorithm for non-homogeneous populationAlibabaie Najmeh0Ghasemzadeh Mohammad1Meinel Christoph2Computer Department, Engineering Campus, Yazd UniversityAssoc. Prof. at Yazd University in Iran and Guest Researcher at HPIPresident and CEO of Hasso Plattner Institute (HPI), at Potsdam UniversitySelection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups consisting of elements with similar structure. Elements in a group have the same size, while elements in different groups are of different sizes. The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers. In the proposed method, the crossover and mutation operators can accept parents with different sizes; this can lead to versatility in population and information transfer among sub-populations. We implemented the proposed method and evaluated its performance against some random datasets and the Ruspini dataset as well. The experimental results show that the proposed method could effectively determine the appropriate number of clusters and recognize their centers. Overall this research implies that using heterogeneous population in the genetic algorithm can lead to better results.http://dx.doi.org/10.1051/itmconf/20170902001 |
| spellingShingle | Alibabaie Najmeh Ghasemzadeh Mohammad Meinel Christoph A variant of genetic algorithm for non-homogeneous population ITM Web of Conferences |
| title | A variant of genetic algorithm for non-homogeneous population |
| title_full | A variant of genetic algorithm for non-homogeneous population |
| title_fullStr | A variant of genetic algorithm for non-homogeneous population |
| title_full_unstemmed | A variant of genetic algorithm for non-homogeneous population |
| title_short | A variant of genetic algorithm for non-homogeneous population |
| title_sort | variant of genetic algorithm for non homogeneous population |
| url | http://dx.doi.org/10.1051/itmconf/20170902001 |
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