Abstract
A new approach for solving the Schrödinger equation based on a microgenetic algorithm (μ-GA) is presented. Feed forward neural network is used to represent the solution, while random point evaluation method (RPEM) is introduced to define the fitness score to be maximized in the μ-GA breeding procedure. Convergence of the final stage of searching is accelerated by invoking the deterministic optimizer. The algorithm is tested in the calculation of one-dimensional harmonic oscillator and double-well potential systems.
| Original language | English |
|---|---|
| Pages (from-to) | 429-438 |
| Number of pages | 10 |
| Journal | Chemical Physics Letters |
| Volume | 327 |
| Issue number | 5-6 |
| Publication status | Published - 2000 Sep 15 |
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ASJC Scopus subject areas
- Physical and Theoretical Chemistry
- Spectroscopy
- Atomic and Molecular Physics, and Optics
Cite this
Numerical solution of the Schrödinger equation by a microgenetic algorithm. / Nakanishi, H.; Sugawara, Michihiko.
In: Chemical Physics Letters, Vol. 327, No. 5-6, 15.09.2000, p. 429-438.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Numerical solution of the Schrödinger equation by a microgenetic algorithm
AU - Nakanishi, H.
AU - Sugawara, Michihiko
PY - 2000/9/15
Y1 - 2000/9/15
N2 - A new approach for solving the Schrödinger equation based on a microgenetic algorithm (μ-GA) is presented. Feed forward neural network is used to represent the solution, while random point evaluation method (RPEM) is introduced to define the fitness score to be maximized in the μ-GA breeding procedure. Convergence of the final stage of searching is accelerated by invoking the deterministic optimizer. The algorithm is tested in the calculation of one-dimensional harmonic oscillator and double-well potential systems.
AB - A new approach for solving the Schrödinger equation based on a microgenetic algorithm (μ-GA) is presented. Feed forward neural network is used to represent the solution, while random point evaluation method (RPEM) is introduced to define the fitness score to be maximized in the μ-GA breeding procedure. Convergence of the final stage of searching is accelerated by invoking the deterministic optimizer. The algorithm is tested in the calculation of one-dimensional harmonic oscillator and double-well potential systems.
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UR - http://www.scopus.com/inward/citedby.url?scp=0001182569&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0001182569
VL - 327
SP - 429
EP - 438
JO - Chemical Physics Letters
JF - Chemical Physics Letters
SN - 0009-2614
IS - 5-6
ER -