Abstract: A technique for solving combinatorial problems, such as vehicle routing for multiple vehicles integrates evolutionary algorithms and reinforcement learning. A genetic algorithm maintains a set of solutions for the problem and improves the solutions using mutation (modify a solution) and crossover (combine two solutions). The best solution is selected from the improved set of solutions. A system that integrates evolutionary algorithms, such as a genetic algorithm, and reinforcement learning comprises two components. A first component is a beam search technique for generating solutions using a reinforcement learning model. A second component augments a genetic algorithm using learning-based solutions that are generated by the reinforcement learning model. The learning-based solutions improve the diversity of the set which, in turn, improves the quality of the solutions computed by the genetic algorithm.

Abstract: Systems and methods in accordance with the present disclosure can implement a parallel processing system, such as a graphics processing unit (GPU)-based system, to generate solutions to complex computational problems. Aspects of this technical solution can retrieve a plurality of solutions each representing a plurality of values in a multi-dimensional space, allocate, to one or more processing units associated with the one or more circuits and having a parallelized configuration, one or more of the plurality of solutions, modify, by the one or more processing units according to the parallelized configuration, at least one value of the one or more solutions allocated to the one or more processing units, to determine a plurality of modified solutions, and output, from the plurality of modified solutions, according to one or more criteria indicating a diversity among the plurality of modified solutions, a selected solution.