Many important modern engineering problems involve satisfying multiple objectives. Simultaneous optimization of these objectives can be difficult as they compete for the same set of any given resources. One way to solve multiple-objective optimization is with the use of genetic algorithms (GA’s).
One can break down the structure of these multi-objective genetic algorithms (MOGA’s) into two different approaches. One approach is based on incorporating multiple objectives into a single fitness function which will evaluate how well a given solution solves the issue. The other approach uses multiple fitness functions, each representing a different objective, which when combined create a solution set of possible solutions to the problem. This project focuses on combining these approaches in order to make a hybrid model, which can benefit from combining the results of the previous two methods; incorporating a level of automation that allows for inference of a final solution based on different prioritization of each objective. This solution would not have been previously attainable by either standalone method.
This project is named the Automated Inferential Multi-Objective Optimization System (AIMOS), and it can be applied to a multitude of different problem types. In order to show its capabilities, AIMOS has been applied to a theoretical optimization problem used to measure the effectiveness of GA’s.
|Commitee:||Antonios, Imad, Lancor, Lisa|
|School:||Southern Connecticut State University|
|School Location:||United States -- Connecticut|
|Source:||MAI 56/02M(E), Masters Abstracts International|
|Subjects:||Logic, Artificial intelligence, Computer science|
|Keywords:||Game theory, Genetic algorithms|
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