Cartesian genetic programming: its status and future

Miller, Julian F.

doi:10.1007/s10710-019-09360-6

Cited by 75 publications

(36 citation statements)

References 102 publications

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“…CGP is a subtype of EAs, which generates directed acyclic computational configurations of nodes. Like other types of EAs, it uses trees as its solution representation (Miller, 2020 ). CGP can evolve symbolic expressions, Boolean logic circuits, and artificial neural networks.…”

Section: Methodsmentioning

confidence: 99%

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Looze¹,

Dehsarvi²,

Crosby³

et al. 2021

Front. Aging Neurosci.

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Background: Increasing efforts have focused on the establishment of novel biomarkers for the early detection of Alzheimer’s disease (AD) and prediction of Mild Cognitive Impairment (MCI)-to-AD conversion. Behavioral changes over the course of healthy ageing, at disease onset and during disease progression, have been recently put forward as promising markers for the detection of MCI and AD. The present study examines whether the temporal characteristics of speech in a collaborative referencing task are associated with cognitive function and the volumes of brain regions involved in speech production and known to be reduced in MCI and AD pathology. We then explore the discriminative ability of the temporal speech measures for the classification of MCI and AD.Method: Individuals with MCI, mild-to-moderate AD and healthy controls (HCs) underwent a structural MRI scan and a battery of neuropsychological tests. They also engaged in a collaborative referencing task with a caregiver. The associations between the conversational speech timing features, cognitive function (domain-specific) and regional brain volumes were examined by means of linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of the conversational speech features.Results: MCI and mild-to-moderate AD are characterized by a general slowness of speech, attributed to slower speech rate and slower turn-taking in conversational settings. The speech characteristics appear to be reflective of episodic, lexico-semantic, executive functioning and visuospatial deficits and underlying volume reductions in frontal, temporal and cerebellar areas.Conclusion: The implementation of conversational speech timing-based technologies in clinical and community settings may provide additional markers for the early detection of cognitive deficits and structural changes associated with MCI and AD.

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Section: Methodsmentioning

confidence: 99%

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Looze¹,

Dehsarvi²,

Crosby³

et al. 2021

Front. Aging Neurosci.

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“…Genes that make up the genotype (individual representations) in CGP are integers representing where a node receives its data at inputs, the actions on the data are performed at the node. The output data appears at the output of nodes for processing of the following nodes [32][33].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

“…In particular, it is a popular and easily adaptable representation method for the solution of many problems. Also CGP is an appropriate method of GP to represent many computational process such as equations, state machines, neural networks, algorithms, and electronic circuits [33].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Ari

Alagöz

2021

Sakarya University Journal of Science

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Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

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“…Cartesian genetic programming (CGP) [49] encodes graphs with linear or grid-based genotypes and produces new ones by evolving the initial graph, originally used produce electronic circuits. A recent line of work [7,8] introduced evolving graphs by graph programming, CGP's generalization to arbitrary graphs.…”

Section: Learning-based Graph Generationmentioning

confidence: 99%

Automated generation of consistent, diverse and structurally realistic graph models

et al. 2021

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In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.

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Cartesian genetic programming: its status and future

Cited by 75 publications

References 102 publications

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Automated generation of consistent, diverse and structurally realistic graph models

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