- You can apply this algorithm to problems having linear, non-linear or integer constraints.
- You can choose among a set of options for fitness scaling, creation, selection, crossover, and mutation.
- You can also customize by proving your own functions for creation, selection, and mutation.
Genetic Algorithm Selection for large scale problem
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I want to solve large-scale optimization problems using a Genetic Algorithm.
Can you please advise me on whether using MATLAb's built-in optimization toolbox is better than creating my own genetic algorithm script as I may need to vectorize and also may be at a later stage hybridize it by combining it with some search algorithm or exact method.
Aritra el 22 de Nov. de 2022
As per my understanding you are trying to get insights on the properties of the Genetic Algorithm shipped in Global Optimization Toolbox.
The Global Optimization Toolbox in MATLAB comes with pre-implemented Genetic Algorithm which can be used for solving various types of smooth/non-smooth problems with any types of constraints.
For detail, please see this MathWorks documentation below for more information on Custom Options and Outputs in Genetic Algorithm: https://in.mathworks.com/help/gads/options-and-outputs.html
For detail, please see this MathWorks documentation below for more information on Custom Data Type Optimization Using Genetic Algorithm: https://in.mathworks.com/help/gads/custom-data-type-optimization-using-ga.html