Semantic Geometric Initialization

Pawlak, Tomasz P.; Krawiec, Krzysztof

doi:10.1007/978-3-319-30668-1_17

Cited by 12 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the terminology of [17,18], GSC only has the possibility of generating a globally optimal solution only if this solution lays within the semantic convex hull identified by the population. The need for overcoming this drawback has led to several methods to properly initialize a population of GSGP, like for instance the ones presented in [19,20,21].…”

Section: Geometric Semantic Genetic Programmingmentioning

confidence: 99%

“…The same happens in GP, where a wide variety of programs of various sizes and shapes are desirable [13]. With the introduction of GSOs, new techniques taking their particularities into consideration, have been developed [19]. The Evolutionary Demes Despeciation Algorithm (EDDA) is contextualized in this research track.…”

Section: Evolutionary Demes Despeciation Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

Bakurov

Castelli

Vanneschi

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.

show abstract

Section: Geometric Semantic Genetic Programmingmentioning

confidence: 99%

Section: Evolutionary Demes Despeciation Algorithmmentioning

confidence: 99%

Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

Bakurov

Castelli

Vanneschi

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The Semantic Geometric Initialization (SGI) [17], on the other hand, generates a set S of semantics, such that the desired output is guaranteed to belong to the convex hull of S. These semantics are generated by adding or subtracting an offset to O in different combinations of the semantic space dimensions. Then, for each semantics s i ∈ S, the method generates an individual whose semantics is equal to s i .…”

Section: Related Workmentioning

confidence: 99%

A Generic Framework for Building Dispersion Operators in the Semantic Space

Oliveira

Otero

Pappa

2018

Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Recently, researchers began to explore the effect of introducing semanticawareness in different fundamental parts of the evolutionary process to enhance the performance of GP. These methods mainly cover the population initialisation [21,176] and the selection process [89].…”

Section: Semantic-awareness In Other Fundamental Components Of Gpmentioning

confidence: 99%

Improving the Generalisation of Genetic Programming for Symbolic Regression

Chen¹

View full text Add to dashboard Cite

<p>Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.</p>

show abstract

Semantic Geometric Initialization

Cited by 12 publications

References 15 publications

Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

A Generic Framework for Building Dispersion Operators in the Semantic Space

Improving the Generalisation of Genetic Programming for Symbolic Regression

Contact Info

Product

Resources

About