A Survey of Probabilistic Model Building Genetic Programming

Shan, Yin; McKay, Bob; Essam, Daryl; Abbass, Hussein A.

doi:10.1007/978-3-540-34954-9_6

Cited by 48 publications

(25 citation statements)

References 34 publications

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“…Hence, the search in the space of heuristics or algorithms can be performed by any other type of search method, given a suitable equivalent of GP's function set with sufficient expressive power. Some interesting alternatives to GP to construct programs include the use of a variation of Estimation of Distribution Algorithms called Estimation of Distribution Programming [104] and a variation of Ant Colony Optimisation called Ant Programming [99,100]. Although ant algorithms have been used as a hyper-heuristic to select heuristics [21,29,33,62], to the best of our knowledge neither technique has been used as a hyper-heuristic to generate heuristics, and this could be an interesting research direction.…”

Section: Algorithm Spacementioning

confidence: 99%

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

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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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.

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Section: Algorithm Spacementioning

confidence: 99%

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

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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“…3 For detailed background in this area, and a survey of publications up to 2005, we refer the interested reader to Shan et al [89]. For the sake of convenience, we refer to the general field as EDA-GP.…”

Section: Probabilistic Model-building Algorithmsmentioning

confidence: 99%

Grammar-based Genetic Programming: a survey

McKay

Hoài

Whigham

et al. 2010

Genet Program Evolvable Mach

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138

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Publication informationGenetic Programming and Evolvable Machines, Abstract Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.

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“…These algorithms (Shan et al 2006;McKay et al 2010;Bosman and de Jong 2008) depart from the traditional uses of probabilistic graphical models since the probability distributions are often associated with the grammar rules and their different contexts of application. For example, Bosman and de Jong (2008) estimate the distribution of programming trees based on the subtrees that actually occur in the data.…”

Section: Model-based Genetic Programmingmentioning

confidence: 99%

“…The representation specifies a set of rules whose expansion leads to trees, and the probability distributions are defined on these rules. For a good review of these algorithms, see Shan et al (2006) and McKay etal. (2010).…”

Section: Model-based Genetic Programmingmentioning

confidence: 99%

A review on probabilistic graphical models in evolutionary computation

et al. 2012

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Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

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A Survey of Probabilistic Model Building Genetic Programming

Cited by 48 publications

References 34 publications

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

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

Grammar-based Genetic Programming: a survey

A review on probabilistic graphical models in evolutionary computation

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