Upper confidence tree for planning restart strategies in multi-modal optimization

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In the context of gradient-free multi-modal optimization, numerous algorithms are based on restarting evolution strategies. Such an algorithm classically performs many local searches, for finding all the global optima of the objective function. The strategy used to select the restarting points (i.e., the initial points of the local searches) is a crucial step of the method. In previous works, a strategy based on reinforcement learning has been proposed: the search space is partitioned and a multi-armed bandit algorithm is used to select an interesting region to sample. This strategy significantly improves the main optimization algorithm but is limited to small dimensional problems. In this paper, we propose an algorithm tackling this problem. The main contributions are (1) a tree-based scheme for hierarchically partition the search space and (2) a multi-armed bandit strategy for traversing this tree. Thus, a node in the tree corresponds to a region of the search space, and its children partition this region according to one dimension. The multi-armed bandit strategy is used to traverse the tree by selecting interesting children recursively. We have experimented our algorithm on difficult multi-modal functions, with small and large dimensions. For small dimensions, we observe performances comparable to previous state-of-the-art algorithms. For large dimensions, we observe better results as well as lower memory consumption.