Genetic diversity as an objective in multi-objective evolutionary algorithms.
A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.
Toffolo
Andrea
A
Department of Mechanical Engineering, University of Padova Via Venezia 1, 35131 Padova, Italy. andrea.toffolo@unipd.it
Benini
Ernesto
E
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D014644:Genetic Variation; D008957:Models, Genetic; D015233:Models, Statistical