TY - JOUR

T1 - Numerical solution of the Schrödinger equation by a microgenetic algorithm

AU - Nakanishi, H.

AU - Sugawara, M.

N1 - Funding Information:
This work is supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science and Culture and, in part, by `Research and Development Applying Advanced Computational Science and Technology' of Japan Science and Technology Corporation. Authors are grateful to Dr. D.L. Carroll for his Fortran GA driver, which is open to the public on the Internet.

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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U2 - 10.1016/S0009-2614(00)00913-1

DO - 10.1016/S0009-2614(00)00913-1

M3 - Article

AN - SCOPUS:0001182569

SN - 0009-2614

VL - 327

SP - 429

EP - 438

JO - Chemical Physics Letters

JF - Chemical Physics Letters

IS - 5-6

ER -