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We have used a population of 20 individuals, the number of generations was 40, the crossover ratio was 0.8 and the mutation probability was 0.7.
A genetic function algorithm tool was used for variables selection [17], which is a mathematical technique served to reduce the number of variables used in the data set, as well as to select only the pertinent ones, in which mutation probability was 0.5 the smoothing parameter was 1.0, and cross over probability was 1.0.
Only when mutation probability was constant with male age were female preference and relative male somatic quality positively correlated.
As reported previously [8], when mutation probability was constant across all male age classes, females exhibited a preference for older males and a bias against younger males (Fig. 1A).
When mutation probability was a linear function of male age, female preference functions did not differ between simulations with a constant mutation rate and those with a low mutation rate (Fig. 1A), because gamete mutation load changed with male age similarly in both cases (Fig. 1C).
The threshold for crossover probability used here was 0.8 and the mutation probability was 0.05.
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The Mutation Probability is the probability that each gene from a child will be mutated.
A mutation probability is also specified, which can randomly alter the children's genes.
And, crossover probability and mutation probability are adjusted dynamically by fuzzy inferences, which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.
The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed.
In HDNA-GA, three new crossover operators, replacement operator, transposition operator and reconstruction operator, are designed to improve the population diversity, and the mutation operator with adaptive mutation probability is applied to guarantee against stalling at local peak.
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