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020 _a9781461468462
_9978-1-4614-6846-2
024 7 _a10.1007/978-1-4614-6846-2
_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 X
_h[electronic resource] /
_cedited by Rick Riolo, Ekaterina Vladislavleva, Marylyn D Ritchie, Jason H. Moore.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXXVI, 242 p.
_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 _aEvolving SQL Queries from Examples with Developmental Genetic Programming -- A Practical Platform for On-Line Genetic Programming for Robotics -- Cartesian Genetic Programming for Image Processing -- A new mutation paradigm for Genetic Programming -- Introducing an Age-Varying Fitness Estimation Function -- EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System -- Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-Processing -- Meta-dimensional analysis of phenotypes using the Analysis Tool for Heritable and Environmental Network Associations -- A Baseline Symbolic Regression Algorithm -- Symbolic Regression Model Comparison Approach Using Transmitted Variation -- A Framework for the Empirical Analysis of Genetic Programming System Performance -- More or Less? Two Approaches to Evolving Game-Playing Strategies -- Symbolic Regression is Not Enough -- FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud -- Representing Communication and Learning in Femtocell Pilot Power Control Algorithms.
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 _aVladislavleva, Ekaterina.
_eeditor.
700 1 _aRitchie, Marylyn D.
_eeditor.
700 1 _aMoore, Jason H.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461468455
830 0 _aGenetic and Evolutionary Computation,
_x1932-0167
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-6846-2
912 _aZDB-2-SCS
942 _2Dewey Decimal Classification
_ceBooks
999 _c44736
_d44736