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 |
| DOIs | |
| Publication status | Published - 2000 Sep 15 |
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Physical and Theoretical Chemistry
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Cite this
Nakanishi, H., & Sugawara, M. (2000). Numerical solution of the Schrödinger equation by a microgenetic algorithm. Chemical Physics Letters, 327(5-6), 429-438. https://doi.org/10.1016/S0009-2614(00)00913-1