The evolution of PBIL algorithm when used to solve the nuclear reload optimization problem

Author:

da Silva Márcio Henrique,Legey Ana Paula,Mól Antônio Carlos de A.

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference16 articles.

1. Baluja, S., 1994, “Population - Based Incremental Learning: A Method for Integrating Genetic Search Based Function optimization and Competitive Learning”, Technical Report, CMU-CS-94-163, June.

2. Genetics algorithms and random keys for sequencing and optimization;Bean;ORSA J. Comput.,1994

3. The computer as a physical system: a microscopic quantum mechanical hamiltonian model of computers as represented by turing machines;Benioff;J. Stat. Phys.,1980

4. Parameterless evolutionary algorithm applied to the nuclear reload problem;Caldas;Ann. Nucl. Energy,2008

5. A New Approach to the use of genetic algorithms to solve the pressurized water reactor’s fuel management optimization problem;Chapot;Ann. Nucl. Energy,1999

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