Genetic Algorithms for Applied Path Planning
| dc.contributor.author | Ragusa, Vincent R. | |
| dc.date.accessioned | 2017-05-29T20:58:32Z | |
| dc.date.available | 2017-05-29T20:58:32Z | |
| dc.date.issued | 2017-05 | |
| dc.description | Honors Thesis Spring 2017 | en_US |
| dc.description.abstract | Path planning is the computational task of choosing a path through an environment. As a task humans do hundreds of times a day, it may seem that path planning is an easy task, and perhaps naturally suited for a computer to solve. This is not the case however. There are many ways in which NP-Hard problems like path planning can be made easier for computers to solve, but the most signi cant of these is the use of approximation algorithms. One such approximation algorithm is called a genetic algorithm. Genetic algorithms belong to a an area of computer science called evolutionary computation. The techniques used in evolutionary computation algorithms are modeled after the principles of Darwinian evolution by natural selection. Solutions to the problem are literally bred for their problem solving ability through many generations of selective breeding. The goal of the research presented is to examine the viability of genetic algorithms as a practical solution to the path planning problem. Various modi cations to a well known genetic algorithm (NSGA-II) were implemented and tested experimentally to determine if the modi cation had an e ect on the operational e ciency of the algorithm. Two new forms of crossover were implemented with positive results. The notion of mass extinction driving evolution was tested with inconclusive results. A path correction algorithm called make valid was created which has proven to be extremely powerful. Finally several additional objective functions were tested including a path smoothness measure and an obstacle intrusion measure, the latter showing an enormous positive result. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11416/321 | |
| dc.publisher | Florida Southern College | en_US |
| dc.subject | Path planning | en_US |
| dc.subject | Genetic algorithms | en_US |
| dc.subject | Evolutionary computation | en_US |
| dc.title | Genetic Algorithms for Applied Path Planning | en_US |
| dc.type | Thesis | en_US |
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