The fitness function and its impact on local search methods

The fitness function is generally defined rather straightforwardly in evolutionary algorithms (EA): it is simply the value of the function to optimize. We argue and show that embedding more information in the fitness function leads to a significant improvement of the quality of the local optima that are reached. The technique is developed here on NP-hard problems and demonstrated on the job-shop scheduling problem. The technique is first used in a mere steepest descent hill-climber in order to assess its usefulness. Then, it is shown that its use in an EA also improves its performance in terms of the quality of solutions that are found