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001 978-3-319-27565-9
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005 20220801222015.0
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008 151215s2016 sz | s |||| 0|eng d
020 _a9783319275659
_9978-3-319-27565-9
024 7 _a10.1007/978-3-319-27565-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aKrawiec, Krzysztof.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959188
245 1 0 _aBehavioral Program Synthesis with Genetic Programming
_h[electronic resource] /
_cby Krzysztof Krawiec.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXI, 172 p. 25 illus., 15 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v618
505 0 _aProgram Synthesis -- Limitations of Conventional Program Evaluation -- The Framework of Behavioral Program Synthesis -- Behavioral Assessment of Test Difficulty -- Semantic Genetic Programming -- Synthesizing Programs with Consistent Execution Traces -- Pattern-guided Program Synthesis -- Behavioral Code Reuse -- Search Drivers -- Experimental Assessment of Search Drivers -- Implications of the Behavioral Perspective -- Future Perspectives.
520 _aGenetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond. .
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aSoftware engineering.
_94138
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aSoftware Engineering.
_94138
710 2 _aSpringerLink (Online service)
_959189
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319275635
776 0 8 _iPrinted edition:
_z9783319275642
776 0 8 _iPrinted edition:
_z9783319801711
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v618
_959190
856 4 0 _uhttps://doi.org/10.1007/978-3-319-27565-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80298
_d80298