Anticipation in Dynamic Optimization: The Scheduling Case.

This contribution addresses the role of anticipation in evolutionary algorithms for dynamic optimization problems. Recent approaches have mainly focused on maintaining the population diversity as a warrant for the ability of tracking the optimum. In our paper, we show that it is also useful to anticipate changes of the environment by explicitly searching for solutions which maintain flexibility. Although this is a valid approach to all dynamic optimization problems, it seems particularly important for optimization problems where a part of the solution is fixed at each step. For the example of job shop scheduling, we suggest a measure of flexibility and show that much better solutions can be obtained when this measure is incorporated into the fitness-function. [ABSTRACT FROM AUTHOR]