Cognizant Technology Solutions U.S. Corporation

Xin Qiu

Risto Miikkulainen

Elliot Meyerson

A computer-implemented method optimizing genetic algorithms for finding solutions to a provided problem is described. The method implements a multi-arm bandit algorithm to determine performance scores for candidate individuals from a candidate pool in dependence on successes and failures of the one or more candidates. The method evolves the candidate individuals in the candidate pool by performing evolution steps including: determining a fitness score for each of the candidate individuals in the candidate pool in dependence on the performance scores for the candidate individuals, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool after the discarding of the candidate individuals. This evolution is repeated evolve the candidate individuals in the candidate pool and one or more candidate individuals from the candidate pool is selected based on best neighborhood performance measures, wherein the selected winning candidate individual is a solution to the provided problem.

Enhancing evolutionary optimization in uncertain environments by allocating evaluations via multi-armed bandit algorithms

A residual estimation with an I/O kernel ('RIO') framework provides estimates of predictive uncertainty of neural networks, and reduces their point-prediction errors. The process captures neural network ('NN') behavior by estimating their residuals with an I/O kernel using a modified Gaussian process (&#x201c;GP&#x201d;). RIO is applicable to real-world problems, and, by using a sparse GP approximation, scales well to large datasets. RIO can be applied directly to any pretrained NNs without modifications to model architecture or training pipeline.

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Xin Qiu