There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features, subdivides the instance into regions and determines which planner to apply in each region. The results obtained with our prototype system were very promising even though it utilized simplistic strategies for all components. Even so, we did determine that strategies for problem subdivision and for combination of partial regional solutions have a crucial impact on performance. In this paper, we propose new methods for these steps to improve the performance of the meta-planner. For problem subdivision, we propose two new methods: a method based on 'gaps' and a method based on information theory. For combining partial solutions, we propose two new methods that concentrate on neighboring areas of the regional solutions. We present results that show the performance gain achieved by utilizing these new strategies.