Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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In this work we present two evolutionary algorithms applied to look for positive water cluster structures. Both algorithms were applied in order to simulate the formation of the aggregates using the neutral clusters as a precursor. In other words, we are not looking for the global minima positive water cluster structures, but rather than trying to find the most stable structures formed from neutral stable clusters. To achieve our goal three steps were executed. In the first one we looked for the most stable structures for (H2O)n(n = 2 - 8), applying a genetic algorithm. In the second step we simulated that the found neutral clusters had lost one of their electrons, creating positive clusters (H2O)+/n. Finally, in the last step we simulated the creation of positive cluster by the aggregation of one positive ion, forming (H2O)nH2O+ clusters. in the latter stage we applied a quantum inspired evolutionary algorithm for numerical optimization (QIEA-R). Results of our search present innovative positive water structures and was able to compare two different ways for ionic cluster formation.