Application of Grammatical Swarm to Symbolic Regression Problem
| Autor: | Hideyuki Sugiura, Risako Yamamoto, Yi Zuo, Eisuke Kita |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computation ComputingMethodologies_MISCELLANEOUS Computer Science::Neural and Evolutionary Computation Particle swarm optimization Swarm behaviour 02 engineering and technology Function (mathematics) Translation (geometry) ComputingMethodologies_ARTIFICIALINTELLIGENCE 020901 industrial engineering & automation Design objective Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Symbolic regression Algorithm Mathematics |
| Zdroj: | Neural Information Processing ISBN: 9783319700922 ICONIP (4) |
| DOI: | 10.1007/978-3-319-70093-9_37 |
| Popis: | Grammatical Swarm (GS), which is one of the evolutionary computations, is designed to find the function, the program or the program segment satisfying the design objective. Since the candidate solutions are defined as the bit-strings, the use of the translation rules translates the bit-strings into the function or the program. The swarm of particles is evolved according to Particle Swarm Optimization (PSO) in order to find the better solution. The aim of this study is to improve the convergence property of GS by changing the traditional PSO in GS with the other PSOs such as Particle Swarm Optimization with constriction factor, Union of Global and Local Particle Swarm Optimizations, Comprehensive Learning Particle Swarm Optimization, Particle Swarm Optimization with Second Global best Particle and Particle Swarm Optimization with Second Personal best Particle. The improved GS algorithms, therefore, are named as Grammatical Swarm with constriction factor (GS-cf), Union of Global and Local Grammatical Swarm (UGS), Comprehensive Learning Grammatical Swarm (CLGS), Grammatical Swarm with Second Global best Particle (SG-GS) and Grammatical Swarm with Second Personal best Particle (SG-GS), respectively. Symbolic regression problem is considered as the numerical example. The original GS is compared with the other algorithms. The effect of the model parameters for the convergence properties of the algorithms are discussed in the preliminary experiments. Then, except for CLGS and UGS, the convergence speeds of the other algorithms are faster than that of the original GS. Especially, the convergence properties of GS-cf and SP-GS are fastest among them. |
| Databáze: | OpenAIRE |
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