Modern heuristic optimization algorithms developed in '90s have been a particular focus of attention because of their simplicity, easy software implementation, and moreover, the interesting phenomena that their performance emerged from the interactions among the particles. In this paper, we see that we can get emergent performance as an optimization algorithm by increasing the number of particles on Evolution Strategy. Considering that, we try to increase the interactions among the particles in order to get better performance. We define parameter tuning rule designing as an optimization problem, and use Genetic Programming to find those for Evolution Strategy. In addition, we evaluate the generated tuning rules using statistical tests and several benchmarks to verify that the proposed methods and the generated rules are effective ones.