Editing Genetic programming (section)

==Meta-genetic programming==
Meta-genetic programming is the proposed [[meta-learning (computer science)|meta-learning]] technique of evolving a genetic programming system using genetic programming itself. It suggests that chromosomes, crossover, and mutation were themselves evolved, therefore like their real life counterparts should be allowed to change on their own rather than being determined by a human programmer. Meta-GP was formally proposed by [[Jürgen Schmidhuber]] in 1987.<ref>{{cite web|url=http://www.idsia.ch/~juergen/diploma.html|title=1987 THESIS ON LEARNING HOW TO LEARN, METALEARNING, META GENETIC PROGRAMMING, CREDIT-CONSERVING MACHINE LEARNING ECONOMY}}</ref> [[Douglas Lenat|Doug Lenat]]'s [[Eurisko]] is an earlier effort that may be the same technique. It is a recursive but terminating algorithm, allowing it to avoid infinite recursion. In the "autoconstructive evolution" approach to meta-genetic programming, the methods for the production and variation of offspring are encoded within the evolving programs themselves, and programs are executed to produce new programs to be added to the population.<ref name="Spector 7–40"/><ref>{{Cite book|title=GECCO '16 Companion : proceedings of the 2016 Genetic and Evolutionary Computation Conference : July 20-24, 2016, Denver, Colorado, USA|others=Neumann, Frank (Computer scientist), Association for Computing Machinery. SIGEVO|date=20 July 2016|isbn=9781450343237|location=New York, New York|oclc=987011786}}</ref>

Critics of this idea often say this approach is overly broad in scope. However, it might be possible to constrain the fitness criterion onto a general class of results, and so obtain an evolved GP that would more efficiently produce results for sub-classes. This might take the form of a meta evolved GP for producing human walking algorithms which is then used to evolve human running, jumping, etc. The fitness criterion applied to the meta GP would simply be one of efficiency.