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Genetic Algorithms

Softopt has developed a flexible and easy-to-use software-library for genetic algorithms, which are algorithms applying the principles of biological evolution of species to optimization problems. It incorporates many advanced features in order to obtain excellent convergence properties.

The library offers all commonly used cross-over and mutation operators for the four typical chromosome representations, which are:
  • string of integers in a given interval,
  • string of real numbers in a given interval,
  • string of integers representing a permutation,
  • string of characters (this is equivalent to case 1 but more natural for many applications)
Secondly, the library contains stand-alone GA-subroutines, which automatically include all common mutation and cross-over operators plus many advanced features and a good choice of default parameter settings with the possibility to change any of them if desired. Problem-specific cross-over and mutation operators can easily be added resulting in a better performing hybrid-GA. Some of the advanced features included in all of the stand-alone GA-subroutines are:
  • elimination of duplicates in a population,
  • dynamic reproduction operator selection,
  • restarts,
  • elitism,
  • steady-state reproduction,
  • re-evaluation of the fitness of chromosomes when the fitness function is noisy.
Thirdly, complex representations, which consist of a combination of the four principle representation methods mentioned above, can be used with the stand-alone GA-subroutines. Such complex representations can be the most natural way to express the search domain of difficult problems.