Considering the Roles of Structure in Problem Solving by Computer

Daida, Jason M.

doi:10.1007/0-387-23254-0_5

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…The tension between structure and content in Genetic Programming (GP) is well documented [27,14,4]. The eventual shape that the structures of the individuals take is affected by the availability of basic building blocks (functions, terminals, and/or subtrees) in the first few generations.…”

Section: Introductionmentioning

confidence: 99%

Abstract functions and lifetime learning in genetic programming for symbolic regression

Azad

Ryan

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong.This paper proposes a simple alternative to this powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals.We demonstrate that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population. Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations.

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Section: Introductionmentioning

confidence: 99%

Abstract functions and lifetime learning in genetic programming for symbolic regression

Azad

Ryan

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Challenges in Open-Ended Problem Solving with Genetic Programming

Daida

Genetic Programming Theory and Practice III

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Abstract:This chapter describes how genetic programming might be integrated as a tool into the human context of discovery. To accomplish this, a comparison is made between GP and a well-regarded strategy in open-ended problem solving. The comparison indicates which tasks and skills are likely to be complemented by GP. Furthermore, the comparison also indicates directions in research that may need to be taken for GP to be further leveraged as a tool that assists discovery.

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Large-Scale, Time-Constrained Symbolic Regression

Korns¹

Genetic Programming Theory and Practice IV

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Considering the Roles of Structure in Problem Solving by Computer

Cited by 5 publications

References 27 publications

Abstract functions and lifetime learning in genetic programming for symbolic regression

Abstract functions and lifetime learning in genetic programming for symbolic regression

Challenges in Open-Ended Problem Solving with Genetic Programming

Large-Scale, Time-Constrained Symbolic Regression

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