A Survey of Self Modifying Cartesian Genetic Programming

Harding, Simon; Banzhaf, Wolfgang; Miller, Julian F.

doi:10.1007/978-1-4419-7747-2_6

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…Evolving the node positions complicates the role of the inputs, however. In SM-CGP, where it also isn't certain the graph will include inputs, program input is a function which nodes can choose [Harding et al, 2010]. In this work, we chose to place input in an evolved space to the left of the linear node space, ensuring that nodes form connections to inputs while allowing the inputs to also form their own connection distributions through evolution.…”

Section: Positional Cartesian Genetic Programmingmentioning

confidence: 99%

Positional Cartesian Genetic Programming

Wilson,

Miller,

Cussat-Blanc

et al. 2018

Preprint

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Cartesian Genetic Programming (CGP) has many modifications across a variety of implementations, such as recursive connections and node weights. Alternative genetic operators have also been proposed for CGP, but have not been fully studied.In this work, we present a new form of genetic programming based on a floating point representation. In this new form of CGP, called Positional CGP, node positions are evolved. This allows for the evaluation of many different genetic operators while allowing for previous CGP improvements like recurrency. Using nine benchmark problems from three different classes, we evaluate the optimal parameters for CGP and PCGP, including novel genetic operators.

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Section: Positional Cartesian Genetic Programmingmentioning

confidence: 99%

Positional Cartesian Genetic Programming

Wilson,

Miller,

Cussat-Blanc

et al. 2018

Preprint

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“…Specialised 'Input' functions are also provided (INP, INPP, SKIP), which manipulate a pointer that indexes the available inputs and return the currently indexed input. A full description can be found in (Harding et al, 2010b) and (Harding et al, 2010a). The output is taken from the last node in the genome.…”

Section: Cartesian Genetic Programmingmentioning

confidence: 99%

Cartesian Genetic Programming for Image Processing

Harding

Leitner

Schmidhuber

2013

Genetic and Evolutionary Computation

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Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.

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“…Self-modifying Cartesian genetic programming In addition to the usual functions, in self-modifying Cartesian genetic programming the genotype includes primitive functions that act on the genotype itself, allowing the phenotype to unfold over time [3]. Self-modifying Cartesian genetic programming is relatively easy to implement, has been reported successful on a variety of problems and also has the nice property that self-modification is only triggered when needed and without external intervention.…”

Section: Exploiting Emergencementioning

confidence: 99%