J.F. Miller

Jagdeep Matharu - 4831400 Seminar 4V82

What is CGP? CGP is a form of Automatic computer program

Evaluation (GP) Developed be Miller and Thompson 1997. Inspired from evaluation of digital circuit. Capable of encoding computer programs, electronic

circuits, neural network.

Representation Programs are represented as directed acyclic graphs

which are encoded in the form of a linear string of integer

Genes are Address in data (Connection genes) Address in a function lookup table (Function genes) Address in output data (Output genes)

Genotype is string of integers. Eg. 0 0 1 1 0 0 1 3 1 2 0 1 0 4 4 2 5 4 2 5 7 3

CGP Genotype

CGP General form

Cont’d

Genotype-to-Phenotype mapping Result from the decoding of a genotype is called

phenotype. Many-to-one genotype to phenotype mapping. Some genes in phenotype can be ignored

Decoding Genotype

Phenotype

Evolution of CGP Genotypes Most CGP system use only mutation. Point-mutation

Mutation rate Gene location is change with other valid random value.

Function with other random valid address of function. Input gene value with valid output from any other node

or terminal node value. Output with address of output of other node in

genotype or terminal node value. Crossover

Cont’d

Evaluation strategies 1+𝜆 algorithm

Cont’d An offspring is always chosen if it is equal as fit or has

better fitness than the parent.

Genetic Redundancy Node redundancy

Genes those are not used in fitness calculation. Functional redundancy

Sub-function that actually may be implemented with fewer nodes

bloat Input redundancy

Node functions are not connected to some of the input node Neutrality

Adaptive evolution may cross regions with poor fitness in fitness landscape.

References “CGP Home.” Accessed November 27, 2012. http://www.cartesiangp.co.uk/ J.F. Miller(ed.), Cartesian Genetic Programming , Natural Computing Series, DOI 10.1007/978-3-642-17310-3 2,