Results 271 to 280 of about 3,048,704 (321)
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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
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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
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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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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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Meta-grammar constant creation with grammatical evolution by grammatical evolution
Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005This study examines the utility of meta-grammar constant generation on a series of benchmark problems. The performance of the meta-grammar approach is compared to a grammar which incorporates grammatical ephemeral random constants, digit concatenation, and an expression based approach.
Ian Dempsey +2 more
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Optimizing combinational logic circuits using Grammatical Evolution
Novel Intelligent and Leading Emerging Sciences Conference, 2021This paper applies Grammatical Evolution (GE) to the optimization of combinational logic circuits on gate-level logic. We demonstrate the ability of GE to evolve complex combinational circuits using gate-level combinational logic and show that GE can ...
Ayman Youssef, B. Majeed, C. Ryan
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Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models
International Symposium on Computers and Communications, 2021The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin.
I. D. Falco +5 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
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Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
Grammatical Evolution is an automatic programming system that is a form of Genetic Programming that uses grammars to evolve structures. These structures can be in any form that can be specified using a grammar, including computer languages, graphs and neural networks.
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Grammatical Evolution is an automatic programming system that is a form of Genetic Programming that uses grammars to evolve structures. These structures can be in any form that can be specified using a grammar, including computer languages, graphs and neural networks.
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Comparing Large Language Models and Grammatical Evolution for Code Generation
GECCO CompanionCode generation is one of the most valuable applications of AI, as it allows for automated programming and "self-building" programs. Both Large Language Models (LLMs) and evolutionary methods, such as Genetic Programming (GP) and Grammatical Evolution ...
L. Custode +3 more
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