Knowledge-Driven Computing: Knowledge Engineering and by Maroua Bouzid, Antoni Ligęza (auth.), Prof. Carlos Cotta,

By Maroua Bouzid, Antoni Ligęza (auth.), Prof. Carlos Cotta, Prof. Simeon Reich, Prof. Robert Schaefer, Prof. Antoni Ligęza (eds.)

Knowledge-Driven Computing constitutes an rising region of extensive learn situated on the intersection of Computational Intelligence and data Engineering with robust mathematical foundations. It embraces tools and methods coming from various computational paradigms, resembling evolutionary computation and nature-inspired algorithms, common sense programming and constraint programming, rule-based structures, fuzzy units etc. using quite a few wisdom illustration formalisms and information processing and computing paradigms is orientated in the direction of the effective solution of computationally advanced and hard difficulties.

The major goal of this quantity has been to collect jointly a variety of contemporary papers supplying new principles and options for a large spectrum of Knowledge-Driven Computing ways. extra accurately, the last word objective has been to gather new wisdom illustration, processing and computing paradigms which may be beneficial to practitioners considering the realm of debate. To this finish, contributions overlaying either theoretical elements and useful suggestions, and working with themes of curiosity for a large viewers, and/or cross-disciplinary learn have been preferred.

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Continuing to read subsequent codons and always mapping the left-most non-terminal the individual finally generates the expression y*x-x-x+x, leaving a number of unused codons at the end of the individual, which are deemed to be introns and simply ignored. A full description of GE can be found in [8]. 4 πGrammatical Evolution The GE mapping process can be divided into a number of sub-components including the transcription and translation processes as outlined in the previous section. The πGE variant of GE replaces the translation process to allow evolution to specify the order in which production rules are mapped as opposed to the strict depth-first, left to right, mapping of the standard GE algorithm.

These constitute the following expression: statute(Nr,Kind,Name). Also, the structural relations between the amending (or the introductory) and the main statutes should be given by means of the clauses: amends(Nr,MainStatuteNr), 34 Jolanta Cybulka and Jacek Martinek introduces(Nr,MainStatuteNr). e. the main or the amending statute). The term ArticleNr is used to represent the article’s number (the textual contents of the article is the “source” of the provision), SectionNr constitutes the section number or zero if the article is not divided into sections, and the last argument, ProvisionNr is the number of the provision.

Comparing the results from the MLP with those of πGE on the initial fitness function (Table 3) suggests that πGE has proven competitive with an MLP methodology, in terms of producing a similar classification accuracy. Benchmark results were also obtained using an LDA methodology. 22% out-of-sample. Again, πGE is competitive against these results in terms of classification accuracy. Comparing the results obtained by the linear classifiers (LDA and πGE) against those of an MLP, suggests that strong non-linearities between the explanatory variables and the dependent variable are not present.

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