Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy
  Diversification
  The lack of diversity in a genetic algorithm's population may lead to a bad
performance of the genetic operators since there is not an equilibrium between
exploration and exploitation. In those cases, genetic algorithms present a fast
and unsuitable convergence.
  In this paper we develop a novel hybrid genetic algorithm which attempts to
obtain a balance between exploration and exploitation. It confronts the
diversity problem using the named greedy diversification operator. Furthermore,
the proposed algorithm applies a competition between parent and children so as
to exploit the high quality visited solutions. These operators are complemented
by a simple selection mechanism designed to preserve and take advantage of the
population diversity.
  Additionally, we extend our proposal to the field of memetic algorithms,
obtaining an improved model with outstanding results in practice.
  The experimental study shows the validity of the approach as well as how
important is taking into account the exploration and exploitation concepts when
designing an evolutionary algorithm.
