Graph structured program evolution with automatically defined nodes

Shirakawa, Shinichi; Nagao, Tomoharu

doi:10.1145/1569901.1570050

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…On the other hand, it imposes a structure on the evolved programs and also requires special operators to create/modify/delete ADRs. A similar approach was proposed in [7], where Automatically Defined Nodes (ADN) were used to generate recursive programs using graph structured program evolution (GRAPE).…”

Section: Related Workmentioning

confidence: 99%

Evolving recursive programs using non-recursive scaffolding

Moraglio

Otero

Johnson

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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Abstract-Genetic programming has proven capable of evolving solutions to a wide variety of problems. However, the successes have largely been with programs without iteration or recursion; evolving recursive programs has turned out to be particularly challenging. The main obstacle to evolving recursive programs seems to be that they are particularly fragile to the application of search operators: a small change in a correct recursive program generally produces a completely wrong program. In this paper, we present a simple and general method that allows us to pass back and forth from a recursive program to an associated nonrecursive program. Finding a recursive program can be reduced to evolving non-recursive programs followed by converting the optimum non-recursive program found to the associated optimum recursive program. This avoids the fragility problem above, as evolution does not search the space of recursive programs. We present promising experimental results on a test-bed of recursive problems.

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Section: Related Workmentioning

confidence: 99%

Evolving recursive programs using non-recursive scaffolding

Moraglio

Otero

Johnson

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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“…[15,8,5,33,22,35]). Additional work has been done on the theoretical analysis of modularity in evolutionary algorithms more generally [34].…”

Section: Modules In Gpmentioning

confidence: 99%

Tag-based modules in genetic programming

Spector

Martin

Harrington

et al. 2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In this paper we present a new technique for evolving modular programs with genetic programming. The technique is based on the use of "tags" that evolving programs may use to label and later to refer to code fragments. Tags may refer inexactly, permitting the labeling and use of code fragments to co-evolve in an incremental way. The technique can be implemented as a minor modification to an existing, general purpose genetic programming system, and it does not require pre-specification of the module architecture of evolved programs. We demonstrate that tag-based modules readily evolve and that this allows problem solving effort to scale well with problem size. We also show that the tag-based module technique is effective even in complex, non-uniform problem environments for which previous techniques perform poorly. We demonstrate the technique in the context of the stack-based genetic programming system PushGP, but we also briefly discuss ways in which it may be used with other kinds of genetic programming systems.

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Memetic Algorithms of Graph-Based Estimation of Distribution Algorithms

Maezawa

Handa

2015

Proceedings in Adaptation, Learning and Optimization

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Graph structured program evolution with automatically defined nodes

Cited by 7 publications

References 14 publications

Evolving recursive programs using non-recursive scaffolding

Evolving recursive programs using non-recursive scaffolding

Tag-based modules in genetic programming

Memetic Algorithms of Graph-Based Estimation of Distribution Algorithms

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