US 2025/0053826 A1 · INTEGRATING EVOLUTIONARY ALGORITHMS AND REINFORCEMENT LEARNING

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.

Claims23

Dependent claims

A computer-implemented method for producing an evolved set of solutions to a combinatorial problem, comprising:

receiving, at a reinforcement learning model, a problem definition and problem constraints for the combinatorial problem;
processing the problem definition and the problem constraints by the reinforcement learning model to generate an initial set of solutions to the combinatorial problem;
and
performing, at a genetic solution processor, genetic operations on the initial set of solutions to produce the evolved set of solutions.

1 more element

Dependencies are inferred from the claim text.

Integrating evolutionary algorithms and reinforcement learning

Life of this patent

Integrating evolutionary algorithms and reinforcement learning

Life of this patent

Abstract

Claims23