Theoretical results in genetic programming: the next ten years?

Poli, Riccardo; Vanneschi, Leonardo; Langdon, William B.; McPhee, Nicholas Freitag

doi:10.1007/s10710-010-9110-5

Cited by 76 publications

(98 citation statements)

References 133 publications

(169 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GP is a soft-computing search technique that allows the model structure to vary during the evolution, which makes it particularly indicated for non-linear and empirical modelling. See Poli et al (2010) for a review of the state of the art in GP. Chen and Kuo (2002) classified the literature on the application of evolutionary computation to economics and finance.…”

Section: Evolutionary Computationmentioning

confidence: 99%

A new approach for the quantification of qualitative measures of economic expectations

2016

View full text Add to dashboard Cite

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. We obtain the best results for Belgium, Norway, Austria, Lithuania, Japan and the United Kingdom.

show abstract

Section: Evolutionary Computationmentioning

confidence: 99%

A new approach for the quantification of qualitative measures of economic expectations

2016

View full text Add to dashboard Cite

show abstract

“…Finally, since genetic programming is an important technique in the approaches explored in this article, the theoretical studies in this area are of relevance [89].…”

Section: Foundational Studiesmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

View full text Add to dashboard Cite

The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

show abstract

“…Genetic Programming (GP) [29,30,31,32] is an automatic programming technique that employs an Evolutionary Algorithm (EA) to search the space of candidate solutions, traditionally represented using expression-tree structures, for the one that optimises some sort of program-performance criterion. The highly expressive representation capabilities of programming languages allows GP to evolve arithmetic expressions that can take the form of regression models.…”

Section: Genetic Programmingmentioning

confidence: 99%

Natural Computing in Computational Finance

2012

Studies in Computational Intelligence

View full text Add to dashboard Cite

Theoretical results in genetic programming: the next ten years?

Cited by 76 publications

References 133 publications

A new approach for the quantification of qualitative measures of economic expectations

A new approach for the quantification of qualitative measures of economic expectations

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Natural Computing in Computational Finance

Contact Info

Product

Resources

About