Optimizing Stochastic and Multiple Fitness Functions
Joseph L. Breeden · The MIT Press eBooks · 1995
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. 1 Stochastic Fitness Functions In the theoretical development of evolutionary computation 1 , the fitness functions considered are almost invariably deterministic functions of a predefined parameter set. In the real-world application of optimization techniques, we must often account for stochastic fitness functions. A simple example of a stochastic fitness function is one measurement drawn from a distribution of rea...