Abstract: Methods, systems, and computer-readable storage media for receiving a dataset, the dataset including a plurality of data values, clustering data values of the plurality of data values into a plurality of input feature clusters in input feature space, training a local multi-task Gaussian process (MTGP) for each input feature cluster to provide optimized hyper-parameters in hyper-parameter space, an optimized hyper-parameter being provided for each input feature cluster, merging data values based on the optimized hyper-parameters, and distances between hyper-parameter clusters in the hyper-parameter space to provide a plurality of merged data values, and providing a LL-MTGP model based on the merged data values.

Abstract: A method and system are presented for configuring a search algorithm for solving a combinatorial optimization problem. The search algorithm has a number of procedural components. Each procedural component is configured using a respective data structure. The data structure has a tree structure, including traversal split nodes, each of which represents a choice to be made when configuring the procedural component of the search algorithm. Configuring the procedural component corresponds to traversing the tree structure, and the traversal is performed automatically. At each of the traversal split nodes, the choice of which direction to take through the tree structure is made randomly, using a respective probabilistic function. Thus, a population of candidate algorithms is generated. These are evaluated, and the probabilistic functions modified.

Abstract: A methodology is presented to address the need for rapid generation and optimization of algorithms that are efficient in solving a given class of problems within the framework of a software environment. The environment incorporates an evolutionary learning methodology which automatically optimizes the configurations of procedural components of the algorithm. In this way, both the efficiency and the quality of algorithm development is enhanced significantly.