### 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 |
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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

*Chemical Physics Letters*,

*327*(5-6), 429-438.

**Numerical solution of the Schrödinger equation by a microgenetic algorithm.** / Nakanishi, H.; Sugawara, Michihiko.

Research output: Contribution to journal › Article

*Chemical Physics Letters*, vol. 327, no. 5-6, pp. 429-438.

}

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.

UR - http://www.scopus.com/inward/record.url?scp=0001182569&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001182569&partnerID=8YFLogxK

M3 - Article

VL - 327

SP - 429

EP - 438

JO - Chemical Physics Letters

JF - Chemical Physics Letters

SN - 0009-2614

IS - 5-6

ER -