Tootfinder

Linear programming bounds in homogeneous spaces, I: Optimal packing density
Maximilian Wackenhuth
https://arxiv.org/abs/2505.23572 https://

Linear programming bounds in homogeneous spaces, I: Optimal packing density
In this article we obtain linear programming bounds for the maximal sphere packing density of commutative spaces. A special case of our results solves a conjecture by Cohn and Zhao on linear programming bounds for sphere packings in hyperbolic space.

Thread and Memory-Safe Programming with CLASS
Lu\'is Caires (Instituto Superior T\'ecnico)
https://arxiv.org/abs/2505.20848 https://

Thread and Memory-Safe Programming with CLASS
CLASS is a proof-of-concept general purpose linear programming language, flexibly supporting realistic concurrent programming idioms, and featuring an expressive linear type system ensuring that programs (1) never misuse or leak stateful resources or memory, (2) never deadlock, and (3) always terminate. The design of CLASS and the strong static guarantees of its type system originates in its Linear Logic and proposition-as-types foundations. However, instead of focusing on its theoretical found…

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Zhixing Huang, Bing Xue, Mengjie Zhang, Jeremy S. Ronney, Keith C. Gordon, Daniel P. Killeen
https://arxiv.org/abs/2505.21901

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistry properties of fish meat. However, it is challenging for existing machine learning techniques to comprehensively discover hidden patterns from fish biomass spectral data since the spectral data often have a lot of noises while the training data are quite limited. To better analyze fish biomass spectral data, this pap…

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Zhixing Huang, Bing Xue, Mengjie Zhang, Jeremy S. Ronney, Keith C. Gordon, Daniel P. Killeen
https://arxiv.org/abs/2505.21901

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistry properties of fish meat. However, it is challenging for existing machine learning techniques to comprehensively discover hidden patterns from fish biomass spectral data since the spectral data often have a lot of noises while the training data are quite limited. To better analyze fish biomass spectral data, this pap…

Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf
https://arxiv.org/abs/2505.21991

Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Genetic programming has undergone rapid development in recent years. However, theoretical studies of genetic programming are far behind. One of the major obstacles to theoretical studies is the challenge of developing a model to describe the relationship between fitness values and program genotypes. In this paper, we take linear genetic programming (LGP) as an example to study the fitness-to-genotype relationship. We find that the fitness expectation increases with fitness supremum over instruc…

Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf
https://arxiv.org/abs/2505.21991

Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Genetic programming has undergone rapid development in recent years. However, theoretical studies of genetic programming are far behind. One of the major obstacles to theoretical studies is the challenge of developing a model to describe the relationship between fitness values and program genotypes. In this paper, we take linear genetic programming (LGP) as an example to study the fitness-to-genotype relationship. We find that the fitness expectation increases with fitness supremum over instruc…