ARP: asexual reproduction programming
Recently meta-heuristic techniques have attracted more attention. Algorithms based on Bio-inspired problems are among the most popular techniques of this field. In meta-heuristic algorithms, Genetic algorithm is one of the most useful. GA uses chromosome representation and operates on the chromosome...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-04-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2020.1807465 |
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| author | Alireza Khanteymoori Fatemeh Alamdar Farzaneh Ghorbani |
| author_facet | Alireza Khanteymoori Fatemeh Alamdar Farzaneh Ghorbani |
| author_sort | Alireza Khanteymoori |
| collection | DOAJ |
| description | Recently meta-heuristic techniques have attracted more attention. Algorithms based on Bio-inspired problems are among the most popular techniques of this field. In meta-heuristic algorithms, Genetic algorithm is one of the most useful. GA uses chromosome representation and operates on the chromosome with crossover and mutation operators. Genetic programming is a form of GA with tree representation for its chromosomes. GP was developed to evolve programming in computers and is a population-based algorithm. But GP is very slow and needs a long time for converging. On the other hand, asexual reproduction optimisation (ARO) is another variant of meta-heuristic algorithms in which convergence to the global optima is done at a fast time. In this paper, we introduced a new method, which is inducted by asexual reproduction with combination to GP. This algorithm is named Asexual Reproduction Programming (ARP). ARP has advantages of both ARO and GP together i.e. the fast convergence time of ARO and the power and flexibility of GP. ARP has fast convergence to global optimum while its error is less than GP. By mathematically analysing and proving, we show the ARP convergence to the global optimum. To assay the efficiency of the ARP, two algorithms were compared on some real-valued symbolic regression problems. Perusing the experimental results demonstrate that ARP outperforms GP in performance and convergence time. |
| format | Article |
| id | doaj-art-ccfbecee4d7648fcbb2c6ccc57be481c |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-ccfbecee4d7648fcbb2c6ccc57be481c2025-08-20T02:19:53ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-04-0133225627710.1080/09540091.2020.18074651807465ARP: asexual reproduction programmingAlireza Khanteymoori0Fatemeh Alamdar1Farzaneh Ghorbani2Bioinformatics Group, Department of Computer Science, University of FreiburgDepartment of Computer Engineering, University of ZanjanDepartment of Computer Engineering, University of ZanjanRecently meta-heuristic techniques have attracted more attention. Algorithms based on Bio-inspired problems are among the most popular techniques of this field. In meta-heuristic algorithms, Genetic algorithm is one of the most useful. GA uses chromosome representation and operates on the chromosome with crossover and mutation operators. Genetic programming is a form of GA with tree representation for its chromosomes. GP was developed to evolve programming in computers and is a population-based algorithm. But GP is very slow and needs a long time for converging. On the other hand, asexual reproduction optimisation (ARO) is another variant of meta-heuristic algorithms in which convergence to the global optima is done at a fast time. In this paper, we introduced a new method, which is inducted by asexual reproduction with combination to GP. This algorithm is named Asexual Reproduction Programming (ARP). ARP has advantages of both ARO and GP together i.e. the fast convergence time of ARO and the power and flexibility of GP. ARP has fast convergence to global optimum while its error is less than GP. By mathematically analysing and proving, we show the ARP convergence to the global optimum. To assay the efficiency of the ARP, two algorithms were compared on some real-valued symbolic regression problems. Perusing the experimental results demonstrate that ARP outperforms GP in performance and convergence time.http://dx.doi.org/10.1080/09540091.2020.1807465meta-heuristic techniquesasexual reproduction optimisationgenetic programmingasexual reproduction programmingconvergence |
| spellingShingle | Alireza Khanteymoori Fatemeh Alamdar Farzaneh Ghorbani ARP: asexual reproduction programming Connection Science meta-heuristic techniques asexual reproduction optimisation genetic programming asexual reproduction programming convergence |
| title | ARP: asexual reproduction programming |
| title_full | ARP: asexual reproduction programming |
| title_fullStr | ARP: asexual reproduction programming |
| title_full_unstemmed | ARP: asexual reproduction programming |
| title_short | ARP: asexual reproduction programming |
| title_sort | arp asexual reproduction programming |
| topic | meta-heuristic techniques asexual reproduction optimisation genetic programming asexual reproduction programming convergence |
| url | http://dx.doi.org/10.1080/09540091.2020.1807465 |
| work_keys_str_mv | AT alirezakhanteymoori arpasexualreproductionprogramming AT fatemehalamdar arpasexualreproductionprogramming AT farzanehghorbani arpasexualreproductionprogramming |