Evolution, development and learning using self-modifying cartesian genetic programming

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

doi:10.1145/1569901.1569998

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…We found, however, that inputs still did not scale particularly well with problem size, so in subsequent papers [17,18,19] we examined another strategy: Three special input functions are now added to the function set: INP, INPP and SKIPINP. When decoding the phenotype graph, a pointer is maintained that refers to an input.…”

Section: Inputs and Outputsmentioning

confidence: 99%

“…This ensures that there is always an input available to be read. 2 Also in earlier work we included an extra binary gene with every node which flagged whether the node could provide a program output [17,18,19]. However, in the work for this paper we have removed output genes and instead introduced a primitive function called OUTPUT that provides a program output.…”

Section: Inputs and Outputsmentioning

confidence: 99%

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Developments in Cartesian Genetic Programming: self-modifying CGP

Harding

Miller

Banzhaf

2010

Genet Program Evolvable Mach

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Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to p.

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Section: Inputs and Outputsmentioning

confidence: 99%

Section: Inputs and Outputsmentioning

confidence: 99%

Developments in Cartesian Genetic Programming: self-modifying CGP

Harding

Miller

Banzhaf

2010

Genet Program Evolvable Mach

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“…In the brain, the 'self-modification rules' are ultimately encoded in the genome. In (Harding et al, 2009b), we set out to use SMCGP to evolve a learning algorithm that could act on itself. The basic question being whether SMCGP can evolve a program that can learnduring the development phase -how to perform a given task.…”

Section: Evolving To Learnmentioning

confidence: 99%

“…During this presentation, the error between the expected and actual output is fed back into the SMCGP program, in order to provide some sort of feedback. Full details of how this was implemented can be found in (Harding et al, 2009b).…”

Section: Evolving To Learnmentioning

confidence: 99%

A Survey of Self Modifying Cartesian Genetic Programming

Harding

Banzhaf

Miller

2010

Genetic Programming Theory and Practice VIII

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Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. In addition to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a variety of different problems, and see that SMCGP is able to solve tasks that require scalability and plasticity. We demonstrate how SMCGP is able to produce results that would be impossible for conventional, static Genetic Programming techniques.

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“…The use of a genotype-phenotype map in Genetic Programming (GP) [13,22] has been used by many within the field [1,5,7,10,11,12,15,23] and a number of variants to the standard tree-based form of GP exist, amongst which some of the most popular are Linear GP [2], Cartesian GP [16] and Grammatical Evolution (GE) [3,20]. GE is a grammar-based form of GP which takes inspiration from DNA Transcription in adopting a mapping from a linear genotype to phenotypic GP trees.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Performance of Different Mapping Orders for GE on the Max Problem

Fagan

Nicolau

Hemberg

et al. 2011

Lecture Notes in Computer Science

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We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position Independent Grammatical Evolution (πGE ) in this problem domain. In πGE the genotype-phenotype map is changed so that the evolutionary algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by πGE is rooted in the way the genotype-phenotype map and the genetic operators used with this mapping interact.

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Evolution, development and learning using self-modifying cartesian genetic programming

Abstract: Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This paper asks: Is it possible to evolve a learning algorithm using SMCGP?

Cited by 14 publications

References 11 publications

Developments in Cartesian Genetic Programming: self-modifying CGP

Developments in Cartesian Genetic Programming: self-modifying CGP

A Survey of Self Modifying Cartesian Genetic Programming

Investigation of the Performance of Different Mapping Orders for GE on the Max Problem

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