Results 11 to 20 of about 3,048,704 (321)

Tree-adjunct grammatical evolution [PDF]

IEEE Congress on Evolutionary Computation, 2010
In this paper we investigate the application of tree-adjunct grammars to grammatical evolution. The standard type of grammar used by grammatical evolution, context-free grammars, produce a subset of the languages that tree-adjunct grammars can produce, making tree-adjunct grammars, expressively, more powerful.
Eoin Murphy, Edgar Galvan-Lopez, Anthony Brabazon   +2 more
exaly   +4 more sources

Weighted Hierarchical Grammatical Evolution [PDF]

IEEE Transactions on Cybernetics, 2020
Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE).
Alberto Bartoli, Mauro Castelli, Eric Medvet   +2 more
semanticscholar   +6 more sources

Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

Algorithms, 2023
Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio ...
Michael Tetteh, Allan de Lima, Jack McEllin, Aidan Murphy, Douglas Mota Dias, Conor Ryan   +5 more
doaj   +3 more sources

Grammatical evolution guided by reinforcement [PDF]

2007 IEEE Congress on Evolutionary Computation, 2007
Grammatical evolution is an evolutionary algorithm able to develop, starting from a grammar, programs in any language. Starting from the point that individual learning can improve evolution, in this paper it is proposed an extension of Grammatical evolution that looks at learning by reinforcement as a learning method for individuals.
Jack Mario Mingo, Ricardo Aler
openaire   +4 more sources

Initialisation in Structured Grammatical Evolution

Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023
Robust initialisation has shown to greatly improve the performance of genetic programming on a wide variety of tasks. Many of these have been adapted to work with grammatical evolution, with varying success.
Aidan Murphy, Nuno Lourenço 0002, Anthony Ventresque   +2 more
openaire   +2 more sources

Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection

Signals, 2022
Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of ...
Vasileios Christou, Ioannis Tsoulos, Alexandros Arjmand, Dimitrios Dimopoulos, Dimitrios Varvarousis, Alexandros T. Tzallas, Christos Gogos, Markos G. Tsipouras, Evripidis Glavas, Avraam Ploumis, Nikolaos Giannakeas   +10 more
doaj   +2 more sources

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

Genetic Programming and Evolvable Machines, 2021
In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution.
Sergio Contador, J. Colmenar, Oscar Garnica, J. M. Velasco, J. I. Hidalgo   +4 more
semanticscholar   +3 more sources

GRAPE: Grammatical Algorithms in Python for Evolution

Signals, 2022
GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures.
Allan de Lima, Samuel Carvalho, Douglas Mota Dias, Enrique Naredo, Joseph P. Sullivan, Conor Ryan   +5 more
doaj   +3 more sources

PonyGE2 [PDF]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE,
Michael Fenton, James McDermott, David Fagan, Stefan Forstenlechner, Erik Hemberg, Michael O'Neill 0001   +5 more
openaire   +4 more sources

gramEvol: Grammatical Evolution in R

Journal of Statistical Software, 2016
We describe an R package which implements grammatical evolution (GE) for automatic program generation. By performing an unconstrained optimization over a population of R expressions generated via a user-defined grammar, programs which achieve a desired ...
Farzad Noorian, Anthony M. de Silva, Philip H. W. Leong   +2 more
doaj   +3 more sources