Exploring the Relationship of the Closeness of a Genetic Algorithm's Chromosome Encoding to Its Problem Space

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

Name thesis:degree_name

MS in Computer Science

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2010

Author dc:creator

• McCullough, Kevin

Contributors dc:contributor

• Franz Kurfess

• Genetic Algorithms
• Artificial Intelligence
• AI
• Other Computer Engineering

Identifier

10.15368/theses.2010.17

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-1257