This paper explores the feasibility of applying a grammatical evolution (GE) system and combines it with the genetic algorithm (GA) to establish the inflow predicting model of De-Chi Reservoir in central Taiwan. First, a GE is an evolutionary automatic programming type system, which can discover relationships among observed data and express them mathematically. Further, a GA was used with this ...

This paper describes an agent-based evolutionary computing technique called GRAEL (Grammar Evolution), that is able to perform different natural language grammar optimization and induction tasks. Two different instantiations of the GRAELenvironment are described in this paper: in GRAEL1 large annotated corpora are used to bootstrap grammatical structure in a society of agents, who engage in a s...

Grammatical Evolution (GE) is a grammar-based GA which generates computer programs. GE has the distinction that its input is a BNF, which permits it to generate programs in any language, of arbitrary complexity. Part of the power of GE is that it is closer to natural DNA than other Evolutionary Algorithms, and thus can beneet from natural phenomena such as a separation of search and solution sp...

Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One most important factors influencing this process model hyperparameters which vary significantly with models datasets. Recently, there has been an increased focus on automatically tuning these...

Electrochemical impedance spectroscopy (EIS) is an important electrochemical technique that used to detect changes and ongoing processes in a given material. The main challenge of EIS interpreting the collected measurements, which can be performed several ways. This article focuses on electrical equivalent circuit (EEC) approach uses grammatical evolution automatically construct EEC produces AC...

There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data. However, these suffer from several issues that make them inconvenient for real-world deployment including a demand large amounts of training On the other hand, some errors based on rules may not necessarily require amount if GEC models can realize generalization. This stud...