000 03722nam a2200517 i 4500
001 6267488
003 IEEE
005 20220712204720.0
006 m o d
007 cr |n|||||||||
008 151229s1996 maua ob 001 eng d
010 _zsn 99008583 (print)
020 _z9780262290791
_qebook
020 _z9780262194235
_qprint
020 _a9780262284127
_qelectronic
035 _a(CaBNVSL)mat06267488
035 _a(IDAMS)0b000064818b44ee
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA76.623
_b.A38 1996eb
082 1 0 _a006
_213
245 0 0 _aAdvances in genetic programming /
_c[edited by] P. Angeline and Kenneth E. Kinnear, Jr.
264 1 _aCambridge, Massachusetts :
_bMIT Press,
_cc[1996]
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[1996]
300 _a1 PDF : :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aComplex adaptive systems.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
515 _aFirst issue is not numbered or dated but constitutes v. 1 and vol. numbering begins copyrighted in 1996.
520 _aGenetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 12/29/2015.
650 0 _aGenetic programming (Computer science)
_vPeriodicals.
_923064
650 0 _aComputer programming
_vPeriodicals.
_923065
655 0 _aElectronic books.
_93294
700 1 _aAngeline, Peter J.,
_eeditor.
_923066
700 1 _aKinnear, Kenneth E., Jr.,
_eeditor.
_923067
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_923068
710 2 _aMIT Press,
_epublisher.
_923069
776 0 8 _iPrint version
_z9780262194235
830 0 _aComplex adaptive systems.
_923070
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267488
942 _cEBK
999 _c73142
_d73142