GP continued to flourish, leading to the first specialist GP journal and three years later (2003) the annual Genetic Programming Theory and Practice (GPTP) workshop was established by Rick Riolo.
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In 1996, Koza started the annual Genetic Programming conference which was followed in 1998 by the annual EuroGP conference, and the first book in a GP series edited by Koza. In 2010, Koza listed 77 results where Genetic Programming was human competitive. Subsequently, there was an enormous expansion of the number of publications with the Genetic Programming Bibliography, surpassing 10,000 entries. However, it is the series of 4 books by Koza, starting in 1992 with accompanying videos, that really established GP. Koza followed this with 205 publications on “Genetic Programming” (GP), name coined by David Goldberg, also a PhD student of John Holland. This was followed by publication in the International Joint Conference on Artificial Intelligence IJCAI-89. In 1988, John Koza (also a PhD student of John Holland) patented his invention of a GA for program evolution. Īlthough the idea of evolving programs, initially in the computer language Lisp, was current amongst John Holland’s students, it was not until they organised the first Genetic Algorithms (GA) conference in Pittsburgh that Nichael Cramer published evolved programs in two specially designed languages, which included the first statement of modern "tree-based" Genetic Programming (that is, procedural languages organized in tree-based structures and operated on by suitably defined GA-operators). In 1981, Richard Forsyth demonstrated the successful evolution of small programs, represented as trees, to perform classification of crime scene evidence for the UK Home Office. There was a gap of 25 years before the publication of John Holland's 'Adaptation in Natural and Artificial Systems' laid out the theoretical and empirical foundations of the science. The first record of the proposal to evolve programs is probably that of Alan Turing in 1950. It may also be necessary to increase the starting population size and variability of the individuals to avoid pathologies. Multiple runs (dozens to hundreds) are usually necessary to produce a very good result. It may and often does happen that a particular run of the algorithm results in premature convergence to some local maximum which is not a globally optimal or even good solution. Termination of the recursion is when some individual program reaches a predefined proficiency or fitness level. Typically, members of each new generation are on average more fit than the members of the previous generation, and the best-of-generation program is often better than the best-of-generation programs from previous generations.
Then the selection and other operations are recursively applied to the new generation of programs. Some programs not selected for reproduction are copied from the current generation to the new generation.
Mutation involves substitution of some random part of a program with some other random part of a program. The crossover operation involves swapping random parts of selected pairs (parents) to produce new and different offspring that become part of the new generation of programs. The operations are: selection of the fittest programs for reproduction (crossover) and mutation according to a predefined fitness measure, usually proficiency at the desired task. In artificial intelligence, genetic programming ( GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.