- 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.
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Aritra
2022 年 11 月 22 日
Hi Fahim,
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
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