![]() GP has also been applied to evolvable hardware as well as computer programs.ĭeveloping a theory for GP has been very difficult and so in the 1990s GP was considered a sort of outcast among search techniques. These results include the replication or development of several post-year-2000 inventions. Recently GP has produced many novel and outstanding results in areas such as quantum computing, electronic design, game playing, sorting, and searching, due to improvements in GP technology and the exponential growth in CPU power. In the 1990s, GP was mainly used to solve relatively simple problems because it is very computationally intensive. Gianna Giavelli, a student of Koza's, later pionered the use of genetic programming as a technique to model DNA expression. Koza, a main proponent of GP who has pioneered the application of genetic programming in various complex optimization and search problems. This work was later greatly expanded by John R. 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) was given by Nichael L. Later GP-related work grew out of the learning classifier system community, which developed sets of sparse rules describing optimal policies for Markov decision processes. Fogel, one of the earliest practitioners of the GP methodology, applied evolutionary algorithms to the problem of discovering finite-state automata. John Holland was highly influential during the 1970s. Ingo Rechenberg and his group were able to solve complex engineering problems through evolution strategies as documented in his 1971 PhD thesis and the resulting 1973 book. In the 1960s and early 1970s, evolutionary algorithms became widely recognized as optimization methods. ![]() In 1954, GP began with the evolutionary algorithms first used by Nils Aall Barricelli applied to evolutionary simulations.
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