Description Details References
Function optimization through Genetic Algorithms.
Given a real-based or permutation-based function, and the
associated search space, gaoptim
will perform a
function maximization using the Genetic Algorithm
approach. For better performance, a real-number encoding
is used.
All you need to get started is to provide a function and
the associated search space - there are sensible defaults
to all the other parameters. On the other hand, you can
provide custom genetic-operators to control how your
population will reproduce
and mutate
(see
the examples).
After setting the algorithm parameters, you can evolve your population and check the results. You don't need to do this in one step, you can always evolve a small number of generations and query the best solution found. If this solution doesn't fit your needs, you can keep evolving your population - this approach saves time and computer resources.
Randy L. Haupt, Sue Ellen Haupt (2004). Practical genetic algorithms - 2nd ed.
Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs - 3rd ed.
Luke, Sean. Department of Computer Science. George Mason University. Essentials of Metaheuristics - online version 1.2, July 2011.
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