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639:. MEP strength consists in 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
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variant encoding multiple solutions in the same chromosome. MEP representation is not specific (multiple representations have been tested). In the simplest variant, MEP chromosomes are linear strings of instructions. This representation was inspired by
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When the chromosome is evaluated it is unclear which instruction will provide the output of the program. In many cases, a set of programs is obtained, some of them being completely unrelated (they do not have common instructions).
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Note that, for many problems, this evaluation has the same complexity as in the case of encoding a single solution in each chromosome. Thus, there is no penalty in running time compared to other techniques.
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MEPX is a cross-platform (Windows, macOS, and Linux Ubuntu) free software for the automatic generation of computer programs. It can be used for data analysis, particularly for solving
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Each instruction contains a variable, a constant, or a function. If the instruction is a function, then the arguments (given as instruction's addresses) are also present.
835:", 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
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of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be
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In MEP, the best of them (which has the lowest error) will represent the chromosome. This is different from other GP techniques: In
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848:", NASA/DoD Conference on Evolvable Hardware, 24–26 June, Seattle, Edited by R. Zebulum (et al.), pages 87-90, IEEE Press, NJ, 2004
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694:, the fitness is the sum of differences (in absolute value) between the expected output (called target) and the actual output.
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is a new open source library implementing Multi
Expression Programming technique in Haskell programming language.
630:(MEP) is an evolutionary algorithm for generating mathematical functions describing a given set of data. MEP is a
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is a free and open source library implementing Multi
Expression Programming technique. It is written in C++.
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Which expression will represent the chromosome? Which one will give the fitness of the chromosome?
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Here is a simple MEP chromosome (labels on the left side are not a part of the chromosome):
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For the above chromosome, here is the list of possible programs obtained during decoding:
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The fitness (or error) is computed in a standard manner. For instance, in the case of
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It may require cleanup to comply with
Knowledge's content policies, particularly
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E1 = a, E2 = b, E4 = c, E5 = d, E3 = a + b. E6 = c + d. E7 = (a + b) * d.
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Evolving
Evolutionary Algorithms using Multi Expression Programming
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Each instruction is evaluated as a possible output of the program.
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Please help to demonstrate the notability of the topic by citing
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the gene providing the output is evolved like all other genes.
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Evolving
Digital Circuits using Multi Expression Programming
822:", Technical report, Univ. Babes-Bolyai, Cluj-Napoca, 2002
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MEP chromosomes are arrays of instructions represented in
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Covariance Matrix
Adaptation Evolution Strategy (CMA-ES)
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A major contributor to this article appears to have a
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variants encoding a single solution in a chromosome.
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may be too technical for most readers to understand
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709:the last instruction will give the output. In
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306:Learn how and when to remove these messages
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467:reliable, independent, third-party sources
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615:Learn how and when to remove this message
597:Learn how and when to remove this message
540:Learn how and when to remove this message
524:, without removing the technical details.
485:Learn how and when to remove this message
415:Learn how and when to remove this message
1149:No free lunch in search and optimization
866:Multi Expression Programming source code
461:by replacing them with more appropriate
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1223:Regression and curve fitting software
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522:make it understandable to non-experts
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113:Promoter based genetic algorithm
954:Cellular evolutionary algorithm
295:or discuss these issues on the
48:Cellular evolutionary algorithm
364:"Multi expression programming"
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1050:Bacterial Colony Optimization
786:Cartesian genetic programming
711:Cartesian Genetic Programming
199:Cartesian genetic programming
118:Spiral optimization algorithm
1228:Software that uses wxWidgets
820:Multi Expression Programming
628:Multi Expression Programming
330:general notability guideline
214:Multi expression programming
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1218:Machine learning algorithms
1045:Particle swarm optimization
989:Gene expression programming
818:Oltean M.; Dumitrescu D.: "
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1009:Learning classifier system
999:Natural evolution strategy
801:Linear genetic programming
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698:Fitness assignment process
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326:The topic of this article
204:Linear genetic programming
151:Clonal selection algorithm
103:Natural evolution strategy
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844:Oltean M.; Grosan C.: "
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1099:Artificial development
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662:Example of MEP program
58:Differential evolution
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29:Evolutionary algorithm
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575:neutral point of view
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632:Genetic Programming
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653:Three-address code
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1159:Mating pool
909:Main Topics
740:time-series
587:August 2016
530:August 2015
475:August 2016
405:August 2016
341:independent
1197:Categories
946:Algorithms
742:problems.
459:improve it
449:verifiable
375:newspapers
349:redirected
292:improve it
146:Chromosome
579:talk page
463:citations
339:that are
298:talk page
176:Selection
156:Crossover
1174:Journals
775:See also
721:Software
655:format.
161:Mutation
21:a series
19:Part of
516:Please
453:neutral
389:scholar
353:deleted
229:Eurisko
758:Libmep
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345:merged
224:Schema
23:on the
807:Notes
396:JSTOR
382:books
351:, or
769:hmep
764:hmep
738:and
726:MEPX
451:and
368:news
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