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Found 4 papers, 4 papers with code
Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
However, these algorithms require a large number of evaluations to provide a quality solution, which might be computationally expensive when the evaluation cost is high.
Generalized Early Stopping in Evolutionary Direct Policy Search
We test the introduced stopping criterion in five direct policy search environments drawn from games, robotics and classic control domains, and show that it can save up to 75% of the computation time.
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common.
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments.
