Vector-valued function estimation by grammatical evolution for autonomous robot control

Burbidge, Robert; Wilson, M.

doi:10.1016/j.ins.2013.09.044

Cited by 11 publications

(3 citation statements)

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“…An appropriate and good method constructs classification programs in a human-readable format using the technique of grammatical evolution [9]. Grammatical evolution is an evolutionary process that has been applied with success in many areas, such as music composition [10], economics [11], symbolic regression [12], robot control [13], and combinatorial optimization [14]. The method used here was initially described in [15] and the code is also described in [16].…”

Section: Introductionmentioning

confidence: 99%

Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution

2023

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Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data transmission over the Internet. Artificial intelligence (AI) and machine learning techniques play a crucial role in classifying patterns derived from sensor data. Environmental stations offer a multitude of parameters that can be obtained to uncover hidden patterns showcasing the impact of pollution on the surrounding environment. This paper focuses on utilizing the CO parameter as an indicator of pollution in two datasets collected from wireless environmental monitoring devices in the greater Port area and the Town Hall of Igoumenitsa City in Greece. The datasets are normalized to facilitate their utilization in classification algorithms. The k-means algorithm is applied, and the elbow method is used to determine the optimal number of clusters. Subsequently, the datasets are introduced to the grammatical evolution algorithm to calculate the percentage fault. This method constructs classification programs in a human-readable format, making it suitable for analysis. Finally, the proposed method is compared against four state-of-the-art models: the Adam optimizer for optimizing artificial neural network parameters, a genetic algorithm for training an artificial neural network, the Bayes model, and the limited-memory BFGS method applied to a neural network. The comparison reveals that the GenClass method outperforms the other approaches in terms of classification error.

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Section: Introductionmentioning

confidence: 99%

Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution

2023

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“…The first of these is the work of Koza (1992), who carried out the first substantial studies of GP across many problem areas and created the canonical tree-based form of GP that is the foundation for many variants of and applications of GP (Montana 1995;O'Neill and Ryan 2001;Ferreira 2001;He et al 2011a, b;Du et al 2014;Alfonseca and Gil 2013;Burbidge and Wilson 2014;Fernandez-Blanco et al 2013;Oltean et al 2009;Howard et al 2011;Harman et al 2012;Langdon and Harman 2015;Mckay et al 2010) even today. The notion of types was introduced into GP by Montana (1995) in the Strongly Typed Genetic Programming (STGP) system.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these issues and allow the use of a system of types in GP, Montana (1995) allowed the typing terminals and operator arguments in the second phase of the development process of GP, presenting the so-called STGP approach; however, no basic change was made to the program representations when compared to the approach of Koza. Finally, the work by O'Neill and Ryan (2001) on GE aroused a wide range of interest among GP researchers and practitioners (Burbidge and Wilson 2014;Hugosson et al 2010;Oltean et al 2009;RiscoMartin et al 2014;Wilson and Kaur 2009). In fact, compared to other two kinds of GP methods discussed, grammar-related approaches can be best suited to type descriptions and recursive program generations.…”

Section: Introductionmentioning

confidence: 99%

Model approach to grammatical evolution: theory and case study

Deng

Wang

et al. 2015

Soft Comput

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Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of Communicated by V. Loia. B Pei He an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.

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Analyzing Grammatical Evolution and $$\pi $$ π Grammatical Evolution with Grammar Model

Deng

Gao

et al. 2016

Advances in Intelligent Systems and Computing

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Vector-valued function estimation by grammatical evolution for autonomous robot control

Cited by 11 publications

References 32 publications

Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution

Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution

Model approach to grammatical evolution: theory and case study

Analyzing Grammatical Evolution and $$\pi $$ π Grammatical Evolution with Grammar Model

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