A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits
Usually, important parameters in the design and implementation of combinational logic circuits are the number of gates, transistors, and the levels used in the design of the circuit. In this regard, various evolutionary paradigms with different competency have recently been introduced. However, whil...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2018-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1059_fcefe618fe62de43246b018c7bd01c09.pdf |
| _version_ | 1818648847962865664 |
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| author | A. M. Mousavi M. Khodadadi |
| author_facet | A. M. Mousavi M. Khodadadi |
| author_sort | A. M. Mousavi |
| collection | DOAJ |
| description | Usually, important parameters in the design and implementation of combinational logic circuits are the number of gates, transistors, and the levels used in the design of the circuit. In this regard, various evolutionary paradigms with different competency have recently been introduced. However, while being advantageous, evolutionary paradigms also have some limitations including: a) lack of confidence in reaching at the correct answer, b) long convergence time, and c) restriction on the tests performed with higher number of input variables. In this paper, we have implemented a genetic programming approach that given a Boolean function, outputs its equivalent circuit such that the truth table is covered and the minimum number of gates (and to some extent transistors and levels) are used. Furthermore, our implementation improves the aforementioned limitations by: Incorporating a self-repairing feature (improving limitation a); Efficient use of the conceivable coding space of the problem, which virtually brings about a kind of parallelism and improves the convergence time (improving limitation b). Moreover, we have applied our method to solve Boolean functions with higher number of inputs (improving limitation c). These issues are verified through multiple tests and the results are reported. |
| first_indexed | 2024-12-17T01:24:56Z |
| format | Article |
| id | doaj.art-7acbc4acb8d54537989567437d8af0f6 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-17T01:24:56Z |
| publishDate | 2018-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-7acbc4acb8d54537989567437d8af0f62022-12-21T22:08:43ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442018-07-016235536310.22044/jadm.2017.10591059A Fast and Self-Repairing Genetic Programming Designer for Logic CircuitsA. M. Mousavi0M. Khodadadi1Department of Electrical Engineering, Lorestan University, Khoramabad, Lorestan, Iran.Department of Electrical Engineering, Azad University, Arak Branch, Arak, Iran.Usually, important parameters in the design and implementation of combinational logic circuits are the number of gates, transistors, and the levels used in the design of the circuit. In this regard, various evolutionary paradigms with different competency have recently been introduced. However, while being advantageous, evolutionary paradigms also have some limitations including: a) lack of confidence in reaching at the correct answer, b) long convergence time, and c) restriction on the tests performed with higher number of input variables. In this paper, we have implemented a genetic programming approach that given a Boolean function, outputs its equivalent circuit such that the truth table is covered and the minimum number of gates (and to some extent transistors and levels) are used. Furthermore, our implementation improves the aforementioned limitations by: Incorporating a self-repairing feature (improving limitation a); Efficient use of the conceivable coding space of the problem, which virtually brings about a kind of parallelism and improves the convergence time (improving limitation b). Moreover, we have applied our method to solve Boolean functions with higher number of inputs (improving limitation c). These issues are verified through multiple tests and the results are reported.http://jad.shahroodut.ac.ir/article_1059_fcefe618fe62de43246b018c7bd01c09.pdfGenetic ProgrammingLogical CircuitsDesign Optimization |
| spellingShingle | A. M. Mousavi M. Khodadadi A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits Journal of Artificial Intelligence and Data Mining Genetic Programming Logical Circuits Design Optimization |
| title | A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits |
| title_full | A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits |
| title_fullStr | A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits |
| title_full_unstemmed | A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits |
| title_short | A Fast and Self-Repairing Genetic Programming Designer for Logic Circuits |
| title_sort | fast and self repairing genetic programming designer for logic circuits |
| topic | Genetic Programming Logical Circuits Design Optimization |
| url | http://jad.shahroodut.ac.ir/article_1059_fcefe618fe62de43246b018c7bd01c09.pdf |
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