Representation and parameterisation issues in genetic algorithms

  • Jason E. Price

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

A multi-chromosome GA (Multi-GA) was developed, based upon concepts from the natural world, allowing improved flexibility in a number of areas including representation, genetic operators, their parameter rates and real world multi-dimensional applications.
A series of experiments were conducted, comparing the performance of the Multi-GA to a traditional GA on a number of recognised and increasingly complex test optimisation surfaces, with promising results. Further experiments demonstrated the Multi-GA's flexibility through the use of non-binary chromosome representations and its applicability to dynamic parameterisation. A number of alternative and new methods of dynamic parameterisation were investigated, in addition to a new non-binary 'Quotient crossover' mechanism.
Finally, the Multi-GA was applied to two real world problems, demonstrating its ability to handle mixed type chromosomes within an individual, the limited use of a chromosome level fitness function, the introduction of new genetic operators for structural self-adaptation and its viability as a serious real world analysis tool.
The first problem involved optimum placement of computers within a building, allowing the Multi-GA to use multiple chromosomes with different type representations and different operators in a single individual.
The second problem, commonly associated with Geographical Information Systems (GIS), required a spatial analysis location of the optimum number and distribution of retail sites over two different population grids. In applying the Multi-GA, two new genetic operators (addition and deletion) were developed and explored, resulting in the definition of a mechanism for self-modification of genetic material within the Multi-GA structure and a study of this behaviour.
Date of AwardDec 1996
Original languageEnglish
SupervisorAlan Harget (Supervisor)

Keywords

  • multi-chromosome
  • optimisation
  • spatial analysis
  • Geographical Information Systems
  • dynamic parameterisation

Cite this

Representation and parameterisation issues in genetic algorithms
Price, J. E. (Author). Dec 1996

Student thesis: Doctoral ThesisDoctor of Philosophy