Toward Evolving Dispatching Rules With Flow Control Operations By Grammar-Guided Linear Genetic Programming
Linear genetic programming (LGP) has been successfully applied to dynamic job shop
scheduling (DJSS) to automatically evolve dispatching rules. Flow control operations are …
scheduling (DJSS) to automatically evolve dispatching rules. Flow control operations are …
Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based
estimation of distribution genetic programming approach that uses denoising autoencoder …
estimation of distribution genetic programming approach that uses denoising autoencoder …
Evolving Equation Learner For Symbolic Regression
Symbolic regression, a multifaceted optimization challenge involving the refinement of both
structural components and coefficients, has gained significant research interest in recent …
structural components and coefficients, has gained significant research interest in recent …
Pretraining reduces runtime in denoising autoencoder genetic programming by an order of magnitude
J Reiter, D Schweim, D Wittenberg - Proceedings of the Companion …, 2023 - dl.acm.org
Denoising autoencoder genetic programming (DAE-GP) is an estimation of distribution
genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term …
genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term …
Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations
Existing genetic programming (GP) methods are typically designed based on a certain
representation, such as tree-based or linear representations. These representations show …
representation, such as tree-based or linear representations. These representations show …