Guervós, Juan Julián Merelo2, Adamidis, Panagiotis2, Beyer, Hans-George2, Fernández-Villacañas Martin, José Luis2, Schwefel, Hans-Poul2
Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few algorithms have used a measure to guide the search. The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results on a set of widely used benchmark problems, not only in terms of fitness, but more important: The DGEA saved a substantial amount of fitness evaluations compared to the simple EA, which is a critical factor in many real-world applications.
Proceedings of the 7th International Conference on Parallel Problem Solving From Nature: Parallel Problem Solving From Nature - Ppsn Vii, 2002, p. 462-471