Robust Optimization and Groundwork for Problem Mapping

<p>Goals of this research were to develop a conceptual algorithm that can optimize execution time for generating a solution set and demonstrate that a solution set of one sub-problem can be applied to another sub-problem within the same problem set. To achieve the proposed goals, GloPro was developed to generate rule sets for different sub-problems within a problem set, as well as identifying which rule sets are to be utilized for a given instance of the problem. The algorithm was to be robust, as to be applicable to a wide array of problems without radical re-design per problem. This idea was fueled by the concept of Structure-Mapping Theory, where a set of knowledge is mapped from one domain to another based on the shared baseline characteristics. Utilizing a Genetic Algorithm (GA), plus A* with a classifier hybrid, the algorithm includes a period of supervised learning followed by execution in an operational environment. Progressive learning occurred through application of the algorithm to multiple sub-problems, each having unique characteristics. The algorithm was applied to a simulated robotic agent in a maze environment as a proxy for other problems. This problem is well known, but still an active problem in the field of robotics. The experimental results indicate that the hybrid GA with A* technique is feasible, and that progressive learning is enhanced through application of previous learning results to a period of learning. In addition, the evolved solutions were unique to the sub-problems, indicating that this technique can be used to develop robust solutions across sub-problems.</p>

Name thesis:degree_name

Master of Science in Unmanned and Autonomous Systems Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Electrical, Computer, Software, and Systems Engineering

Year

2017

Author dc:creator

• Ghelarducci, Leo Austin

• optimization
• groundwork
• problem mapping
• Electrical and Computer Engineering

Repository record dc:identifier

https://commons.erau.edu/edt/328

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1327