gaoptim: Genetic Algorithm optimization

In gaoptim: Genetic Algorithm optimization for real-based and permutation-based problems

Description

Function optimization through Genetic Algorithms.

Details

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.

References

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.

gaoptim documentation built on May 30, 2017, 12:37 a.m.