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Enhancing Island Model Genetic Programming by Controlling Frequent Trees
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| Zeitschriftentitel: | Journal of Artificial Intelligence and Soft Computing Research |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Journal of Artificial Intelligence and Soft Computing Research, 9, 2019, 1, S. 51-65 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro |
|---|---|
| author |
Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro |
| spellingShingle |
Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro Journal of Artificial Intelligence and Soft Computing Research Enhancing Island Model Genetic Programming by Controlling Frequent Trees Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| author_sort |
ono, keiko |
| spelling |
Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0024 <jats:title>Abstract</jats:title> <jats:p>In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit <jats:italic>f</jats:italic> requent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its <jats:italic>a</jats:italic>ctivation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.</jats:p> Enhancing Island Model Genetic Programming by Controlling Frequent Trees Journal of Artificial Intelligence and Soft Computing Research |
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10.2478/jaiscr-2018-0024 |
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Walter de Gruyter GmbH, 2019 |
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Journal of Artificial Intelligence and Soft Computing Research |
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| title |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_unstemmed |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_full |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_fullStr |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_full_unstemmed |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_short |
Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_sort |
enhancing island model genetic programming by controlling frequent trees |
| topic |
Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| url |
http://dx.doi.org/10.2478/jaiscr-2018-0024 |
| publishDate |
2019 |
| physical |
51-65 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit <jats:italic>f</jats:italic> requent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its <jats:italic>a</jats:italic>ctivation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.</jats:p> |
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| author | Ono, Keiko, Hanada, Yoshiko, Kumano, Masahito, Kimura, Masahiro |
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| description | <jats:title>Abstract</jats:title> <jats:p>In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit <jats:italic>f</jats:italic> requent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its <jats:italic>a</jats:italic>ctivation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.</jats:p> |
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| imprint_str_mv | Walter de Gruyter GmbH, 2019 |
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| spelling | Ono, Keiko Hanada, Yoshiko Kumano, Masahito Kimura, Masahiro 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0024 <jats:title>Abstract</jats:title> <jats:p>In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit <jats:italic>f</jats:italic> requent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its <jats:italic>a</jats:italic>ctivation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.</jats:p> Enhancing Island Model Genetic Programming by Controlling Frequent Trees Journal of Artificial Intelligence and Soft Computing Research |
| spellingShingle | Ono, Keiko, Hanada, Yoshiko, Kumano, Masahito, Kimura, Masahiro, Journal of Artificial Intelligence and Soft Computing Research, Enhancing Island Model Genetic Programming by Controlling Frequent Trees, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| title | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_full | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_fullStr | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_full_unstemmed | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_short | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| title_sort | enhancing island model genetic programming by controlling frequent trees |
| title_unstemmed | Enhancing Island Model Genetic Programming by Controlling Frequent Trees |
| topic | Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| url | http://dx.doi.org/10.2478/jaiscr-2018-0024 |