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Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code
Lecture Notes in Computer Science, 2004This study examines the possibility of evolving the grammar that Grammatical Evolution uses to specify the construction of a syntactically correct solution. As the grammar dictates the space of symbols that can be used in a solution, its evolution represents the evolution of the genetic code itself.
Michael O'Neill +2 more
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Generative Art via Grammatical Evolution
International Genetic Improvement Workshop, 2023Generative art produces artistic output via algorithmic design. Common examples include flow fields, particle motion, and mathematical formula visualization.
Erik M. Fredericks +2 more
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AVERT: An Automatic Verilog Testbench Generation Tool for Grammatical Evolution
Irish Signals and Systems Conference, 2022With the ever-growing complexity of digital circuits, the appeal of using Machine Learning in digital circuit design has grown significantly. Over the last 25 years, Evolutionary Algorithms have been used to create digital circuits in a field known as ...
Jack McEllin, R. Conway, C. Ryan
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IEEE Transactions on Evolutionary Computation, 2001
We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program.
Michael O'Neill 0001, Conor Ryan
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We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program.
Michael O'Neill 0001, Conor Ryan
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Crossover in Grammatical Evolution
Genetic Programming and Evolvable Machines, 2003Summary: We present an investigation into crossover in Grammatical Evolution that begins by examining a biologically-inspired homologous crossover operator that is compared to standard one and two-point operators. Results demonstrate that this homologous operator is no better than the simpler one-point operator traditionally adopted. An analysis of the
Michael O'Neill 0001 +3 more
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Grammatical evolution for constraint synthesis for mixed-integer linear programming
Swarm and Evolutionary Computation, 2021The Mixed-Integer Linear Programming models are a common representation of real-world objects. They support simulation within the expressed bounds using constraints and optimization of an objective function.
Tomasz P. Pawlak, M. O’Neill
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IEEE Congress on Evolutionary Computation, 2021
Deep Convolutional Neural Networks (CNNs) have reached the attention in the last decade due to their successful application to many computer vision domains. Several handcrafted architectures have been proposed in the literature, with increasing depth and
Cleber A.C.F. da Silva +7 more
semanticscholar +1 more source
Deep Convolutional Neural Networks (CNNs) have reached the attention in the last decade due to their successful application to many computer vision domains. Several handcrafted architectures have been proposed in the literature, with increasing depth and
Cleber A.C.F. da Silva +7 more
semanticscholar +1 more source
Time is On The Side of Grammatical Evolution
International Conference on Computational Collective Intelligence, 2021The computational complexity of Evolutionary Algorithms (EAs) is a well-known concern. This paper is concerned with the resource consumption of GELAB, a novel Grammatical Evolution (GE) system implemented in Matlab.
Aidan Murphy +4 more
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Evolvability in grammatical evolution
Proceedings of the Genetic and Evolutionary Computation Conference, 2017Evolvability is a measure of the ability of an Evolutionary Algorithm (EA) to improve the fitness of an individual when applying a genetic operator. Other than the specific problem, many aspects of the EA may impact on the evolvability most notably the genetic operators and, if present, the genotype-phenotype mapping function. Grammatical Evolution (GE)
MEDVET, Eric +2 more
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Evolutionary Computing based Analysis of Diversity in Grammatical Evolution
International Conference on Adaptive and Intelligent Systems, 2021Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population.
Ayman Youssef +4 more
semanticscholar +1 more source