Joseph Breeden

Paper #: 95-02-027

How does one optimize a fitness function when the values it generates have a stochastic component? How does one simultaneously optimize multiple fitness criteria? These questions are important for many applications of evolutionary computation in an experimental environment. Solutions to these problems are presented along with discussion of situations where they arise, such as modeling and genetic programming. A detailed numerical example from control theory is also provided. In the process, we find that population-based search algorithms are well suited to such problems.