In this work we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation’s average size. Diversity of size within a GA’s population is shown to be a necessary condition for a reduction in mean chromosome length to take place.

We show how a finite variable-length GA under random selection pressure uses 1) diversity of size within the population, 2) over-production of shorter than average individuals, and 3) stochastic errors in selection sampling to naturally reduce the average size of individuals within a population from one generation to the next. In addition to our findings, this work provides GA researchers and practitioners with 1) a number of mathematical tools for analyzing possible size reductions for various matings and 2) new ideas to explore in the area of bloat control.

• Major: Computer Science
• Educational Career:
  • B.S., University of Florida, 1979
• Committee in Charge:
  • Dr. Annie S. Wu
  • Dr. Euripides Montagne
  • Dr. Ali Orooji
  • Dr. David Pratt