000 04246nam a22005655i 4500
001 978-1-4939-0375-7
003 DE-He213
005 20200421111839.0
007 cr nn 008mamaa
008 140401s2014 xxu| s |||| 0|eng d
020 _a9781493903757
_9978-1-4939-0375-7
024 7 _a10.1007/978-1-4939-0375-7
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aGenetic Programming Theory and Practice XI
_h[electronic resource] /
_cedited by Rick Riolo, Jason H. Moore, Mark Kotanchek.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXIV, 227 p. 68 illus., 32 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 _aGenetic and Evolutionary Computation,
_x1932-0167
505 0 _aExtreme Accuracy in Symbolic Regression -- Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer's Disease -- Optimizing a Cloud Contract Portfolio using Genetic Programming-based Load Models -- Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data -- Grounded Simulation: Using Simulated Evolution to Guide Embodied Evolution -- Applying Genetic Programming in Business Forecasting -- Explaining Unemployment Rates with Symbolic Regression -- Uniform Linear Transformation with Repair and Alternation in Genetic Programming -- A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data -- Gaining Deeper Insights in Symbolic Regression -- Geometric Semantic Genetic Programming for Real Life Applications -- Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis.
520 _aThese contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud - communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions - model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining different types of data. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
650 0 _aComputer science.
650 0 _aComputer programming.
650 0 _aComputers.
650 0 _aAlgorithms.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aTheory of Computation.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aProgramming Techniques.
700 1 _aRiolo, Rick.
_eeditor.
700 1 _aMoore, Jason H.
_eeditor.
700 1 _aKotanchek, Mark.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781493903740
830 0 _aGenetic and Evolutionary Computation,
_x1932-0167
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-0375-7
912 _aZDB-2-SCS
942 _cEBK
999 _c55468
_d55468