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Multi Expression Programming (MEP) is a genetic programming variant employing a linear representation of solutions. MEP representation is inspired by Three-address code. MEP introduces a unique feature: the ability to encode multiple solutions, of a problem, in the same chromosome. In this way one can explore larger zones of the search space. For most of the problems this advantage comes with no running-time penalty compared with genetic programming variants encoding a single solution in a chromosome.〔Oltean M.; Dumitrescu D.: "(Multi Expression Programming )", Technical report, Univ. Babes-Bolyai, Cluj-Napoca, 2002〕〔Oltean M.; Grosan C.: "( Evolving Evolutionary Algorithms using Multi Expression Programming )", The 7th European Conference on Artificial Life, September 14–17, 2003, Dortmund, Edited by W. Banzhaf (et al), LNAI 2801, pp. 651-658, Springer-Verlag, Berlin, 2003〕〔Oltean M.; Grosan C.: "(Evolving Digital Circuits using Multi Expression Programming )", NASA/DoD Conference on Evolvable Hardware, 24–26 June, Seattle, Edited by R. Zebulum (et. al), pages 87-90, IEEE Press, NJ, 2004〕 ==Example of MEP program== Here is a simple MEP program: 1: a 2: b 3: + 1, 2 4: c 5: d 6: + 4, 5 7: * 3, 5 On each line we can have a terminal or a function. In the case of functions we also need pointers to its arguments. When we decode the chromosome we obtain multiple expressions: E1 = a, E2 = b, E4 = c, E5 = d, E3 = a + b. E6 = c + d. E7 = (a + b) * d. Which expression will represent the chromosome? In MEP each expression is evaluated and the best of them will represent the chromosome. For most of the problems, this evaluation has the same complexity as in the case of encoding a single solution in each chromosome. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Multi expression programming」の詳細全文を読む スポンサード リンク
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