2 research outputs found
Improving metaheuristic performance by evolving a variable fitness function.
In this paper we study a complex real world workforce scheduling
problem. We apply constructive search and variable neighbourhood search
(VNS) metaheuristics and enhance these methods by using a variable fitness
function. The variable fitness function (VFF) uses an evolutionary approach to
evolve weights for each of the (multiple) objectives. The variable fitness
function can potentially enhance any search based optimisation heuristic where
multiple objectives can be defined through evolutionary changes in the search
direction. We show that the VFF significantly improves performance of
constructive and VNS approaches on training problems, and ¿learn¿ problem
features which enhance the performance on unseen test problem instances
Evolution of Fitness Functions to Improve Heuristic Performance
Abstract. In this paper we introduce the variable fitness function which can be used to control the search direction of any search based optimisation heuristic where more than one objective can be defined, to improve heuristic performance. The method is applied to a multi-objective travelling salesman problem and the performance of heuristics enhanced with the variable fitness function is compared to the original heuristics, yielding significant improvements. The structure of the variable fitness functions is analysed and the search is visualised to better understand the process