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If the required production levels fall below that rate, operators and machines are being inefficiently used; and if the rate goes too high, operators must work overtime, machine maintenance cannot keep up, breakdowns occur, and the costs of production rise.
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The mutation rate operator presented in this study uses randomness to guide a genetic algorithm (GA) towards the optimal solution.
For both problems, the mutation rate operator function was a sigmoid function fit to the domain x in [0, ∞) and range y in [0, 1].
A theoretical basis and further details of the adaptive mutation rate operator is included in the Theory section under the Discussion.
We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum.
to solving this optimal placement problem, the N-queens problem was chosen in this study as an early practical test of the proposed new mutation rate operator's accuracy.
The adaptive mutation rate operator was tested on three problems: the N-queens problem, the maximization of a 3-dimensional surface, and the minimization of a different 3-dimensional surface (the Rastrigin function).
The best guides require their clients to work their way up to Everest, but cut-rate operators don't, Kodas learned.
Mutation operators are typically of fixed rate, although there is some prior art in the use of variable-rate operators.
The main idea is that variable-rate operators will help algorithms escape the local optima and identify the global optimum more efficiently.
In general NeuronStudio matched the HC standard equally, or better than, the best inter-human match rates (Operator A to Operator B, 85.8% overall), and better than the intra-operator match (Operator C1 C2, 82.9% overall, Table 3).
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