Research Article
Large-Scale Evolutionary Strategy Based on Gradient Approximation
| | Require:: objective function; : initial ; ; | | | Ensure: optimal | | (1) | initial and ; | | (2) | repeat | | (3) | fordo | | (4) | draw sample ; | | (5) | ; | | (6) | evaluate the fitness value | | (7) | end for | | (8) | sort the sampling particles according to the fitness value and compute utilities function | | (9) | compute approximate gradients according to (17)–(20) | | (10) | update parameters use the approximate gradient information | | (11) | update the approximate mean vector | | (12) | update the scalar step size | | (13) | update the covariance factor | | (14) | until the termination criterion |
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