Evolutionary algorithms (EAs) are
search and optimization algorithms based on the principles of natural
evolution, which have found successful applications in bio genetics, computer
science, engineering, economics, chemistry, manufacturing, mathematics, physics
and other fields.
In applying EAs to solve large-scale real world problems, however,
confronted with the conflict between accuracy and speed, EAs often result in an
unsatisfactory compromise. Furthermore, one of the commonest difficulties
encountered is premature convergence.
In a conventional EA, individuals are selected through a fitness-based
procedure, the decrease of population diversity leads to premature convergence.
To solve this problem, the population diversity should be incorporated into the
selection mechanism. If the fitness is pursued excessively, we are actually
conducting a local search, where the diversity declines sharply so that the
global optimization becomes more difficult and even impossible; However, when
we care about the diversity too much, it turns out to be a blindness random
search which does not result in any rise in fitness, leaving us far from the
global optimum. Therefore, we should seek for an appropriate equilibrium point
between individual fitness and population diversity, which seems to be the key
to a successful global optimization.
This reminds us of Yin-Yang theory. Yin and Yang are the dual concepts
originated in ancient Chinese philosophy and metaphysics, which describe two
opposing but complementary principles in all non-static objects and processes
in the universe. Yin and Yang are mutually coupled in equilibrium or even a
harmony to jointly face a same world with shared tasks. It is shown that the
balance between Yin and Yang is crucial to various complex systems such as
human body in terms of health and robustness.
Genetic Algorithms (GAs) with reserve selection mechanism and Adaptive
niching Estimation Distribution Algorithms (EDAs) were developed to balance the
adaptivity and diversity. Strategies were introduced to seek for an optimal
equilibrium point between individual fitness and population diversity.