University of Central Florida STARS Electronic Theses and Dissertations, 2020- 2020 Improving Usability of Genetic Algorithms through Self Adaptation on Static and Dynamic Environments Reamonn Norat University of Central Florida Part of the Computer Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd2020 University of Central Florida Libraries http://library.ucf.edu This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2020- by an authorized administrator of STARS. For more information, please contact
[email protected]. STARS Citation Norat, Reamonn, "Improving Usability of Genetic Algorithms through Self Adaptation on Static and Dynamic Environments" (2020). Electronic Theses and Dissertations, 2020-. 107. https://stars.library.ucf.edu/etd2020/107 IMPROVING USABILITY OF GENETIC ALGORITHMS THROUGH SELF ADAPTATION ON STATIC AND DYNAMIC ENVIRONMENTS by REAMONN NORAT B.S. Embry-Riddle Aeronautical University, 2013 A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Spring Term 2020 c 2020 Reamonn Norat ii ABSTRACT We propose a self-adaptive genetic algorithm, called SAGA, for the purposes of improving the us- ability of genetic algorithms on both static and dynamic problems. Self-adaption can improve usability by automating some of the parameter tuning for the algorithm, a difficult and time- consuming process on canonical genetic algorithms. Reducing or simplifying the need for pa- rameter tuning will help towards making genetic algorithms a more attractive tool for those who are not experts in the field of evolutionary algorithms, allowing more people to take advantage of the problem solving capabilities of a genetic algorithm on real-world problems.