Thus, we created a total of 24 datasets, each with 100 gene-tree/species-tree pairs, and corresponding to a fixed rate of DTL (low-, medium- and high-DTL), a fixed mutation rate (rate-1, rate-3, rate-5, rate-10), and a fixed alignment length.
This constant rate is the mutation rate for neutral alleles.
It was found from the sensitivity analysis that appropriate mutation rate, crossover rate and population size are 1.0%, 25% and 100, respectively.
Experimental results obtained have indicated that a high mutation rate increases the success rate, although computational costs are increased too.
Table 1 Parameters of genetic algorithm Population size Number of iteration Mutation rate Crossover rate 100 200 0.0100.90.
Table 1 Genetic algorithm parameters Parameter name Value Parameter name Value Population size 400 Roulette wheel selection pressure 20 Mutation rate 0.8 Mutation implementation rate 0.8 Crossover rate 0.8 .
The parameters used in the GA include the crossover rate, mutation rate, and population size.
The key parameters are crossover rate, mutation rate, population size, and maximum generations.
The tuned parameters are population, crossover rate, mutation rate, and iteration.
Table 2 Parameters of the NSGA-II Population size Maximum number of generations Crossover rate Mutation rate 100 300 0.95 0.02 Table 3 Parameters of the SPEA-II Population size Maximum number of generations Selection rate Crossover rate Mutation rate 100 400 0.8 0.8 0.2.
Table 1 GP properties Parameter name Parameter specification Population size 500 Mutation frequency 93% Crossover frequency 50% Number of replication 10 Block mutation rate 30% Instruction mutation rate 30% Instruction data mutation rate 40% Homologous crossover 95%.
