GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature

Aaron M Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian

Primary Area: optimization

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Keywords: generative models, combinatorial optimization, end-to-end learning

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TL;DR: We are proposing an end-to-end learning framework for deep generative models that produce objects with intricate combinatorial properties.

Abstract: The generation of diverse but realistic objects that have combinatorial properties has various practical applications across several fields, including computer graphics, animation, industrial design, material science, etc. For instance, we might want to restrict the output of the generator so that it satisfies discrete constraints or encourage certain combinatorial properties as a penalty. However, existing generative models and optimization solvers often struggle to concurrently ensure solution diversity and uphold the underlying combinatorial nature. To address this, we propose $GenCO$, a novel framework that conducts end-to-end training of deep generative models integrated with embedded combinatorial solvers, aiming to uncover high-quality solutions aligned with nonlinear objectives. While structurally akin to conventional generative models, $GenCO$ diverges in its role - it focuses on generating instances of combinatorial optimization problems rather than final objects (e.g., images). This shift allows finer control over the generated outputs, enabling assessments of their feasibility and introducing an additional combinatorial loss component. We demonstrate the effectiveness of our approach on a variety of generative tasks characterized by combinatorial intricacies, including game level generation and map creation for path planning, consistently demonstrating its capability to yield diverse, high-quality solutions that reliably adhere to user-specified combinatorial properties.

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Submission Number: 6999