Cartesian Genetic Programming

Miller, Julian F.

doi:10.1007/978-3-642-17310-3_2

Cited by 179 publications

(195 citation statements)

References 8 publications

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“…For each of the datasets a 1+λ−ES evolutionary algorithm with λ = 4 was used [9] and run for 500 generations. This evolutionary algorithm has a population size of 1 + λ and selects the genotype with the best fitness to be the parent of the new population.…”

Section: Methodsmentioning

confidence: 99%

“…with Mars terrain images [7] and mammograms [13]). CGP is a graph-based form of genetic programming [9]. The genotypes encode directed acyclic graphs and the genes are integers that represent where nodes get their data, what operations nodes perform on the data, and where the output data required by the user is to be obtained.…”

Section: Using Cgp For Classificationmentioning

confidence: 99%

“…Here, we show that using the Mecobo platform it is possible to evolve solutions to two classification problems using materials. To form a basic assessment of effectiveness of the technique we have compared our results with a well-known software-based evolutionary computation technique called Cartesian Genetic Programming (CGP) [9] on the same problems.…”

Section: Introductionmentioning

confidence: 99%

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Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

Mohid

Miller

Harding

et al. 2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. We show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve machine learning classification problems. This is the first time that EIM has been applied to such problems. We evaluate the approach on two standard datasets: Lenses and Iris. Comparing our technique with a well-known software-based evolutionary method indicates that EIM performs reasonably well. We suggest that EIM offers a promising new direction for evolutionary computation.

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Section: Methodsmentioning

confidence: 99%

Section: Using Cgp For Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

Mohid

Miller

Harding

et al. 2014

Parallel Problem Solving From Nature – PPSN XIII

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“…An integer genotype representation, Cartesian Genetic Pro- gramming (CGP), was selected across all problems [19]. Standard CGP uses only a truncation selection strategy followed by uniform mutation without crossover.…”

Section: Algorithm and Representationmentioning

confidence: 99%

“…The range of values given in [19] was used as a basis. The Game of Tag parameters are based on those originally fixed by Reynolds [16].…”

Section: Summary Of Fixed and Variable Parametersmentioning

confidence: 99%

Semantic Bias in Program Coevolution

Seaton

Miller

Clarke

2013

Lecture Notes in Computer Science

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Abstract. We investigate the occurrence of two pathological coevolutionary behaviours, specifically disengagement and cycling, in GP systems. An empirical analysis is carried out using constructed GP problems and a historical pursuit and evasion task, the Game of Tag. The effect of semantic bias on the likelihood of pathologies and performance in a coevolutionary context is examined. We present findings correlating semantic locality in the genotype to phenotype map to disengagement and cycling in a minimal competitive coevolutionary algorithm.

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