Cartesian Genetic Programming: Why No Bloat?

Turner, Andrew; Miller, Julian F.

doi:10.1007/978-3-662-44303-3_19

Cited by 23 publications

(15 citation statements)

References 19 publications

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“…If the fitness function always tests each line of the truth table in the same order, RCGP could in principle use previous inputs to "predict" the correct output. This was shown to be the case in [7]. In an extreme case, RCGP could "predict" the correct outputs without ever considering the program inputs 3 .…”

Section: Implications Of Recurrent Connectionsmentioning

confidence: 93%

“…CGP does not suffer from bloat [6] [7]; a drawback of many GP methods [8]. CGP chromosomes contain non-functioning genes enabling neutral genetic drift during evolution [9] [10].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

“…RCGP has also been used as a method for removing length bias 1 [12] when investigating why CGP does not suffer from bloat [7]. Additionally a form of CGP has been used which implemented a Jordan type architecture [13] for allowing feedback [14].…”

Section: Recurrent Cartesian Genetic Programmingmentioning

confidence: 99%

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Recurrent Cartesian Genetic Programming

Turner

Miller

2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. This paper formally introduces Recurrent Cartesian Genetic Programming (RCGP), an extension to Cartesian Genetic Programming (CGP) which allows recurrent connections. The presence of recurrent connections enables RCGP to be successfully applied to partially observable tasks. It is found that RCGP significantly outperforms CGP on two partially observable tasks: artificial ant and sunspot prediction. The paper also introduces a new parameter, recurrent connection probability, which biases the number of recurrent connections created via mutation. Suitable choices of this parameter significantly improve the effectiveness of RCGP.

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Section: Implications Of Recurrent Connectionsmentioning

confidence: 93%

“…CGP does not suffer from bloat [6] [7]; a drawback of many GP methods [8]. CGP chromosomes contain non-functioning genes enabling neutral genetic drift during evolution [9] [10].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

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Recurrent Cartesian Genetic Programming

Turner

Miller

2014

Parallel Problem Solving From Nature – PPSN XIII

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“…However, it should be noted that CGP has also recently been extended to be capable of creating recurrent (cyclic) computational structures [40][41][42]. It is well-known that CGP does not suffer from bloat [25,38]; a handicap of many GP methods [36]. CGP chromosomes contain explicitly inactive or non-functioning genes which enable ENGD during evolution [47,52].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

Neutral genetic drift: an investigation using Cartesian Genetic Programming

Turner

Miller

2015

Genet Program Evolvable Mach

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Neutral genetic drift is an evolutionary mechanism which can strongly aid the escape from local optima. This makes neutral genetic drift an increasingly important property of Evolutionary Computational methods as more challenging applications are approached. Cartesian Genetic Programming (CGP) is a Genetic Programming technique which contains explicit, as well as the more common implicit, genetic redundancy. As explicit genetic redundancy is easily identified and manipulated it represents a useful tool for investigating neutral genetic drift. The contributions of this paper are as follows. Firstly the paper presents a substantial evaluation of the role and benefits of neutral genetic drift in CGP. Here it is shown that the benefits of explicit genetic redundancy are additive to the benefits of implicit genetic redundancy. This is significant as it indicates that that levels of implicit genetic redundancy present in other Evolutionary Computational methods may be insufficient to fully utilise neutral genetic drift. It is also shown than the identification and manipulation of explicit genetic redundancy is far easier than for implicit genetic redundancy. This is significant as it makes the investigations here possible and leads to new possibilities for allowing more effective use of neutral genetic drift. This is the case not only for CGP, but many other Evolutionary Computational methods which contain explicit genetic redundancy. Finally, it is also shown that neutral genetic drift has additional benefits other than aiding the escape from local optima.

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“…CGP does not suffer from program bloat [43,66]; a recognized drawback of many GP methods [59]. CGP chromosomes contain explicitly inactive or non-functioning genes which are subject to neutral genetic drift aiding the escape from local optima and giving improved navigation of the search landscape [73,81].…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%