Date of Award

3-2010

Degree Name

MS in Computer Science

Department

Computer Science

Advisor

Franz Kurfess

Abstract

For historical reasons, implementers of genetic algorithms often use a haploid binary primitive type for chromosome encoding. I will demonstrate that one can reduce development effort and achieve higher fitness by designing a genetic algorithm with an encoding scheme that closely matches the problem space. I will show that implicit parallelism does not result in binary encoded chromosomes obtaining higher fitness scores than other encodings. I will also show that Hamming distances should be understood as part of the relationship between the closeness of an encoding to the problem instead of assuming they should always be held constant. Closeness to the problem includes leveraging structures that are intended to model a specific aspect of the environment. I will show that diploid chromosomes leverage abeyance to benefit their adaptability in dynamic environments. Finally, I will show that if not all of the parts of the GA are close to the problem, the benefits of the parts that are can be negated by the parts that are not

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