Research Article
Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
| | Input: initialize the chromosome populations | | | Output: the optimal input weights and biases of the hidden layer of the ELM | | (1) | Begin | | (2) | Set the parameters of the ELM | | (3) | Define the fitness function | | (4) | Evaluate the fitness of all chromosomes in population | | (5) | = the best chromosome | | (6) | | | (7) | while < maximum generation do | | (8) | fordo | | (9) | Generate randomly in the interval | | (10) | if< crossover probability then | | (11) | Update the chromosome according to equation (7) | | (12) | end if | | (13) | Generate randomly in the interval | | (14) | if< mutation probability then | | (15) | Update the chromosome according to equation (8) | | (16) | end if | | (17) | Evaluate the chromosome fitness | | (18) | end for | | (19) | Update the chromosome populations according to equation (6) | | (20) | Rank the chromosome populations according to fitness | | (21) | = the best current chromosome | | (22) | | | (23) | end while | | (24) | Return the optimal input weights and biases of the hidden layer of the ELM according to | | (25) | End |
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