sbom-unifier: Integration Framework for Heterogeneous SBOMs
sbom-unifier通过集成和补充多种工具输出及文件级丰富，解决了SBOM生成工具输出异构、字段覆盖不全的问题，提高了SBOM的完整性。
0 citationsRead paper
Selected work
Representative Papers
Novelty-Based Generation of Continuous Landscapes with Diverse Local Optima Networks
Constructing local optima networks (LONs) in continuous optimization is computationally expensive, hindering systematic investigation into the relationship between LON structure and evolutionary algorithm performance. This work addresses this challenge by proposing a non-iterative definition of basins of attraction tailored to the Max-Set of Gaussians fitness landscape, enabling—for the first time—the direct construction of LONs in continuous space. Integrating novelty search, the method efficiently generates a benchmark problem suite with controllable multimodality and diverse graph topologies. The resulting basins of attraction align closely with those obtained via gradient-based methods and effectively predict the success rates of two evolutionary algorithms. This approach provides a high-quality, structurally rich dataset that advances landscape-aware optimization research.
0 citationsRead paper
Recent publications
Latest Papers
sbom-unifier: Integration Framework for Heterogeneous SBOMs
sbom-unifier通过集成和补充多种工具输出及文件级丰富，解决了SBOM生成工具输出异构、字段覆盖不全的问题，提高了SBOM的完整性。
0 citationsRead paper
Novelty-Based Generation of Continuous Landscapes with Diverse Local Optima Networks
Constructing local optima networks (LONs) in continuous optimization is computationally expensive, hindering systematic investigation into the relationship between LON structure and evolutionary algorithm performance. This work addresses this challenge by proposing a non-iterative definition of basins of attraction tailored to the Max-Set of Gaussians fitness landscape, enabling—for the first time—the direct construction of LONs in continuous space. Integrating novelty search, the method efficiently generates a benchmark problem suite with controllable multimodality and diverse graph topologies. The resulting basins of attraction align closely with those obtained via gradient-based methods and effectively predict the success rates of two evolutionary algorithms. This approach provides a high-quality, structurally rich dataset that advances landscape-aware optimization research.
0 citationsRead paper