Abstract
Genetic Algorithms provide computational procedures that are modeled on natural genetic system mechanics, whereby a coded solution is "evolved" from a set of potential solutions, known as a population. GAs accomplish this evolutionary process through the use of basic operators, crossover and mutation. Both the representation of the population and the operators require careful scrutiny, and can change dramatically for different classes of problems. Initial tests were conducted using a GA written in Ada95, and required substantial modifications to handle the changing domains. Subsequent testing was done with a toolbox constructed for Matlab, but the class of problems it can solve is restrictive. Ada95's generic mechanism for parameterization would allow for reuse of existing structures for a broader range of problems. This paper describes the tests performed thus far using both approaches, and compares the performance of the two approaches with regards to optimization.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.