SUMMARY

This research augments current Multiple Objective Evolutionary Algorithms with methods that
dramatically reduce the time required to evolve toward a region of interest in objective
space.

Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization
techniques when the search space is of high dimension and contains many local minima and
maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems.
But, these systems are often computationally expensive to calculate. When these systems
require independent computations to evaluate each objective, the computational expense
grows with each additional objective. This method has developed methods that reduces the
time required for evolution by reducing the number of objective evaluations, while still
evolving solutions that are Pareto optimal. To date, all other Multiple Objective
Evolutionary Algorithms (MOEAs) require the evaluations of all objectives before a fitness
value can be assigned to an individual.

The original contributions of this thesis are:

1. Development of a hierarchical search space description that allows association of
crossover and mutation settings with elements of the genotypic description.

2. Development of a method for parallel evaluation of individuals that removes the need
for delays for synchronization.

3. Dynamical evolution of thresholds for objectives to allow partial evaluation of
objectives for individuals.

4. Dynamic objective orderings to minimize the time required for unnecessary objective
evaluations.

5. Application of MOEAs to the computationally expensive flare pattern design domain.

6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms.

7. Development of a new method of using MOEAs for automatic design of pattern recognition
systems.