In this “Optimization Screen,” you can use the AI model created through AI training and a multi-objective genetic algorithm to search for the optimal input conditions that result in the best possible output.
Size of generation: #
The number of generations for exploring optimal solutions.
Size of population: #
The number of individuals in each generation.
Number of crossover rate: #
The proportion of pairs of individuals with favorable solutions (parents) that exchange genetic information (crossover).
Number of mutation rate: #
The proportion of favorable individuals in each generation that undergo sudden genetic changes (mutation).
Ratio of elite: #
The proportion of outstanding individuals in each generation that will be preserved and reported as the optimization results.
Scale factor: #
A parameter that determines the strength of eliminating individuals with similar output values to maintain diversity within the population. A larger value leads to a wider range of eliminating individuals with similar output values.
