000			03910nam a22004935i 4500
001			978-3-319-27565-9
003			DE-He213
005			20180206182959.0
007			cr nn 008mamaa
008			151215s2016 gw | s |||| 0|eng d
020			_a9783319275659 _9978-3-319-27565-9
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aKrawiec, Krzysztof. _eauthor.
245	1	0	_aBehavioral Program Synthesis with Genetic Programming _h[recurso electrónico] / _cby Krzysztof Krawiec.
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, _x1860-949X ; _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	_aEngineering.
650		0	_aSoftware engineering.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aSoftware Engineering/Programming and Operating Systems.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319275635
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v618
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-27565-9
912			_aZDB-2-ENG
942			_cLIBRO_ELEC
999			_c225962 _d225962