Genetic Algorithm
CU Aerospace
CU Aerospace offers a versatile and modern Genetic Algorithm (GA) for search and optimization, based on natural selection and survival of the fittest principles.
About
A genetic algorithm is a search/optimization technique based on natural selection, where successive generations evolve more fit individuals according to Darwinian theory. CU Aerospace’s genetic algorithm is a computer simulation of such evolution, allowing users to define the environment (function) in which the population must evolve. Originally developed by Dr. David Carroll, this GA was created to provide a FORTRAN-based solution when most other free GAs were in PASCAL, LISP, or C. It incorporates modern GA concepts such as creep mutations, uniform crossover, niching, and elitism, and more recently, the ability to use a micro-GA for efficiency. The “securGA” (small-elitist-creeping-uniform-restarting GA), which proved superior in trials, is now offered as a free download. This tool is particularly useful for those in the FORTRAN community and for various optimization challenges.
Documentation
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