Adaptive Divide-and-Conquer Using Populations and Ensembles Abstract: Many real-world problems are too large and complex for a single monolithic system to solve. The divide-and-conquer strategy has often been used in practice to break a large problem into tractable smaller sub-problems and then solve them. However, useful division of a large and complex problem often requires experienced human experts and rich prior domain knowledge, which are usually unavailable for real-world problems. This talk describes some of our research efforts towards an automatic approach to divide-and-conquer. By evolving and training a team of specialists from random initial conditions, we were able to "decompose" a large and complex problem into simpler ones and solve them without human intervention. Two major approaches will be described. One uses the population structure in evolutionary algorithms, where individuals in a population are evolved into species (i.e., specialists for solving sub-problems). The other uses neural network ensembles in which individual neural networks learn to differentiate from and cooperate with each other. A constructive algorithm for designing ensembles as well as individual neural networks will be introduced.