This thesis introduces Intelligent Fitness Functions and Partial Fitness Functions both
of which can improve the performance of a genetic algorithm which is limited to a fixed
An Intelligent Fitness Function is defined as a fitness function with a memory. The
memory is used to store information about individuals so that duplicate individuals do
not need to have their fitness tested. Different types of memory (long and short term)
and different storage strategies (fitness based, time base and frequency based) have been
tested. The results show that an intelligent fitness function, with a time based long term
memory improves the efficiency of a genetic algorithm the most.
A Partial Fitness Function is defined as a fitness function that only partially tests
the fitness of an individual at each generation. Thus only promising individuals get fully
tested. Using a partial fitness function gives the genetic algorithm more evolutionary
steps in the same length of time as a genetic algorithm using a normal fitness function.
The results show that a genetic algorithm using a partial fitness function can achieve
higher fitness levels than a genetic algorithm using a normal fitness function.
Finally a genetic algorithm designed to solve a substitution cipher is compared to one equipped with an intelligent fitness function and another equipped with a partial
fitness function. The genetic algorithm with the intelligent fitness function and the
genetic algorithm with the partial fitness function both show a significant improvement
over the genetic algorithm with a conventional fitness function.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.