Optimization algorithms and metaheuristics constitute a vital area of computational science, offering robust strategies for tackling complex, multidimensional problems across diverse domains. These ...

Evolutionary algorithms (EAs) represent a class of heuristic optimisation methods inspired by natural selection and Mendelian genetics. They iteratively evolve a population of candidate solutions ...

In the lower part of the figure, it can be seen that L2O leverages on a set of training problem instances from the target optimization problem class to gain knowledge. This knowledge can help identify ...

Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer ...

In a new paper in Science Advances, researchers at JPMorgan Chase, the U.S. Department of Energy's (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

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Many experts believe that once quantum computers are big enough and reliable enough to solve useful problems, the most common deployment architecture will be to have them serve as accelerators for ...