Yin-Yang EAs Balancing Adaptivity and Diversity

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.