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  2. Eureqa - Wikipedia

    en.wikipedia.org/wiki/Eureqa

    Eureqa worked by creating random equations with the data through evolutionary search. [5] Initial guesses might not fit the data well but some of the equations will fit better than others and those will be used as the basis for the next round of guesses until the fit cannot be further improved.

  3. Learning classifier system - Wikipedia

    en.wikipedia.org/wiki/Learning_classifier_system

    A step-wise schematic illustrating a generic Michigan-style learning classifier system learning cycle performing supervised learning. Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm.

  4. Binning (metagenomics) - Wikipedia

    en.wikipedia.org/wiki/Binning_(Metagenomics)

    PCAHIER, [17] another binning algorithm developed by the Georgia Institute of Technology., employs n-mer oligonucleotide frequencies as the features and adopts a hierarchical classifier (PCAHIER) for binning short metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space.

  5. Linear genetic programming - Wikipedia

    en.wikipedia.org/wiki/Linear_genetic_programming

    Linear genetic programming (LGP) [1] is a particular method of genetic programming wherein computer programs in a population are represented as a sequence of register-based instructions from an imperative programming language or machine language. The adjective "linear" stems from the fact that each LGP program is a sequence of instructions and ...

  6. Gene expression programming - Wikipedia

    en.wikipedia.org/wiki/Gene_expression_programming

    From genetic algorithms it inherited the linear chromosomes of fixed length; and from genetic programming it inherited the expressive parse trees of varied sizes and shapes. In gene expression programming the linear chromosomes work as the genotype and the parse trees as the phenotype, creating a genotype/phenotype system .

  7. Tournament selection - Wikipedia

    en.wikipedia.org/wiki/Tournament_selection

    Tournament selection has several benefits over alternative selection methods for genetic algorithms (for example, fitness proportionate selection and reward-based selection): it is efficient to code, works on parallel architectures and allows the selection pressure to be easily adjusted. [2]

  8. List of RNA structure prediction software - Wikipedia

    en.wikipedia.org/wiki/List_of_RNA_structure...

    Name Description Knots [Note 1]Links References trRosettaRNA: trRosettaRNA is an algorithm for automated prediction of RNA 3D structure. It builds the RNA structure by Rosetta energy minimization, with deep learning restraints from a transformer network (RNAformer). trRosettaRNA has been validated in blind tests, including CASP15 and RNA-Puzzles, which suggests that the automated predictions ...

  9. Grammatical evolution - Wikipedia

    en.wikipedia.org/wiki/Grammatical_evolution

    Observe that the objects GE performs search on are the same as those used in genetic algorithms. This means, in principle, that any existing genetic algorithm package, such as the popular GAlib , can be used to carry out the search, and a developer implementing a GE system need only worry about carrying out the mapping from list of integers to ...