Grammatical evolution
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PonyGE2 Open
Grammatical Evolution (GE) is a population-based evolutionary algorithm,\nwhere a formal grammar is used in the genotype to phenotype mapping process.\nPonyGE2 is an open source implementation of GE in Python, developed at UCD's\nNatural C…
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Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models Open
The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific ph…
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On domain knowledge and novelty to improve program synthesis performance with grammatical evolution Open
Programmers solve coding problems with the support of both programming and problem specific knowledge. They integrate this domain knowledge to reason by computational abstraction. Correct and readable code arises from sound abstractions an…
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Particle swarm grammatical evolution for energy demand estimation Open
Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm …
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Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees Open
Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial…
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Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems Open
This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. Th…
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InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting Open
Long-term time series forecasting (LTSF) provides substantial benefits for numerous real-world applications, whereas places essential demands on the model capacity to capture long-range dependencies. Recent Transformer-based models have si…
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Weighted Hierarchical Grammatical Evolution Open
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…
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Towards the evolution of multi-layered neural networks Open
Current grammar-based NeuroEvolution approaches have several shortcomings. On\nthe one hand, they do not allow the generation of Artificial Neural Networks\n(ANNs composed of more than one hidden-layer. On the other, there is no way to\nev…
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Modular Grammatical Evolution for the Generation of Artificial Neural Networks Open
This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation…
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Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar Open
Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached state-of-the-…
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Tonality driven piano compositions with grammatical evolution Open
We present a novel method of creating piano melodies with Grammatical Evolution (GE). The system employs a context free grammar in combination with a tonality-driven fitness function to create a population of piano melodies. The grammar is…
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The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems Open
Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this …
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A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment Open
Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary compu…
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Structured grammatical evolution for glucose prediction in diabetic patients Open
Structured grammatical evolution is a recent grammar-based genetic programming variant that tackles the main drawbacks of Grammatical Evolution, by relying on a one-to-one mapping between each gene and a non-terminal symbol of the grammar.…
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GRAPE: Grammatical Algorithms in Python for Evolution Open
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 ess…
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Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios Open
In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to mod…
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Probabilistic Grammatical Evolution Open
Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to addr…
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AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text Open
The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) pro…
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An Evolutionary Approach to Class Disjointness Axiom Discovery Open
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Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach Open
Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs composed of more than one hidden-layer. On the other, there is no way to evolv…
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QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution Open
This paper presents and analyzes a programming tool that implements a method for classification and function regression problems. This method builds new features from existing ones with the assistance of a hybrid algorithm that makes use o…
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Human in the Loop Fuzzy Pattern Tree Evolution Open
Fuzzy pattern trees evolved using grammatical evolution, a system we call Fuzzy Grammatical Evolution, are shown to be a robust Explainable Artificial Intelligence technique. Experimental results show Fuzzy Grammatical Evolution achieves c…
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Adaptive CGFs Based on Grammatical Evolution Open
Computer generated forces (CGFs) play blue or red units in military simulations for personnel training and weapon systems evaluation. Traditionally, CGFs are controlled through rule-based scripts, despite the doctrine-driven behavior of CG…
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A grammatical evolution approach to the automatic inference of P systems Open
P systems are a bio-inspired framework for defining parallel models of computation. Despite their relevance for both theoretical and application scenarios, the design and the identification of P systems remain tedious and demanding tasks, …
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Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem Open
We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string t…
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Grammatical Evolution for Neural Network Optimization in the Control System Synthesis Problem Open
Grammatical evolution is a perspective branch of the genetic programming. It uses evolutionary algorithm based search engine and Backus – Naur form of domain-specific language grammar specifications to find symbolic expressions. This paper…
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Do Grammatical Error Correction Models Realize Grammatical Generalization? Open
There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data.However, these approaches suffer from several issues that make them inconvenient for realworld deployment including …
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Automatic Machine Learning by Pipeline Synthesis using Model-Based\n Reinforcement Learning and a Grammar Open
Automatic machine learning is an important problem in the forefront of\nmachine learning. The strongest AutoML systems are based on neural networks,\nevolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached\nstate-of-t…
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A Conditional Dependency Based Probabilistic Model Building Grammatical Evolution Open
In this paper, a new approach to grammatical evolution is presented. The aim is to generate complete programs using probabilistic modeling and sampling of (probability) distribution of given grammars. To be exact, probabilistic context fre…