The synergy between exploration and exploitation has been a prominent issue in optimization. Optimization algorithms are generally required to have their parameters tuned to achieve successful exploration-exploitation synergies. Nonetheless, while many algorithms have achieved remarkable success in a wide range of applications, the key to successful exploration-exploitation synergies still remains obscure because conclusions drawn from empirical results or theoretical derivations are usually algorithm specific and/or problem dependent. In our previous studies, a theoretical model based on the concept of local search zones was proposed to provide an alternative perspective depicting the synergy between global search and local search in memetic algorithms. In the present work, we adopt the concept of local search zones to interpret and discuss the effect of population size and selection mechanism, two common design concerns in evolutionary algorithms, on the synergy between exploration and exploitation. In addition to providing interpretations to the effect of population size and selection mechanism on different problem types, this investigation also suggests that with proper mapping, the concept of local search zones is also applicable to delineate the behavior of optimization algorithms with different mechanisms.