Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems
The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-sc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/13/2104 |
| _version_ | 1797480342037200896 |
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| author | Tomasz Praczyk |
| author_facet | Tomasz Praczyk |
| author_sort | Tomasz Praczyk |
| collection | DOAJ |
| description | The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals. |
| first_indexed | 2024-03-09T21:58:17Z |
| format | Article |
| id | doaj.art-09357f39392843a281548e10dc89b4aa |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:58:17Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-09357f39392843a281548e10dc89b4aa2023-11-23T19:52:54ZengMDPI AGElectronics2079-92922022-07-011113210410.3390/electronics11132104Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control ProblemsTomasz Praczyk0Department of Computer Science, Polish Naval Academy, Smidowicza 69, 81-127 Gdynia, PolandThe paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals.https://www.mdpi.com/2079-9292/11/13/2104evolutionary neural networkshill climbcontrolclassification |
| spellingShingle | Tomasz Praczyk Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems Electronics evolutionary neural networks hill climb control classification |
| title | Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems |
| title_full | Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems |
| title_fullStr | Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems |
| title_full_unstemmed | Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems |
| title_short | Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems |
| title_sort | hill climb assembler encoding evolution of small mid scale artificial neural networks for classification and control problems |
| topic | evolutionary neural networks hill climb control classification |
| url | https://www.mdpi.com/2079-9292/11/13/2104 |
| work_keys_str_mv | AT tomaszpraczyk hillclimbassemblerencodingevolutionofsmallmidscaleartificialneuralnetworksforclassificationandcontrolproblems |