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LEADER 00000cam  22002655a 4500 
008    190123s2019    gw     o      001 0 eng   
020    9783030047351 
020    9783030047344 
050 14 Q335|b.G464 2019 
245 00 Genetic Programming Theory and Practice XVI /|cedited by 
       Wolfgang Banzhaf, Lee Spector, Leigh Sheneman. 
250    1st ed. 
260    Cham :|bSpringer International Publishing :|bImprint: 
       Springer,|c2019. 
300    XXI, 234 p. ;|c26 cm. 
490 1  Genetic and Evolutionary Computation 
500    KH-AUL-8 
505 0  1 Exploring Genetic Programming Systems with MAP-Elites --
       2 The Evolutionary Buffet Method -- 3 Emergent Policy 
       Discovery for Visual Reinforcement Learning through 
       Tangled Program Graphs: A Tutorial -- 4 Strong Typing, 
       Swarm Enhancement, and Deep Learning Feature Selection in 
       the Pursuit of Symbolic Regression-Classification -- 5 
       Cluster Analysis of a Symbolic Regression Search Space -- 
       6 What else is in an evolved name? Exploring evolvable 
       specificity with SignalGP -- Lexicase Selection Beyond 
       Genetic Programming -- 8 Evolving developmental programs 
       that build neural networks for solving multiple problems -
       - 9 The Elephant in the Room - Towards the Application of 
       Genetic Programming to Automatic Programming -- 10 
       Untapped Potential of Genetic Programming: Transfer 
       Learning and Outlier Removal -- 11 Program Search for 
       Machine Learning Pipelines Leveraging Symbolic Planning 
       and Reinforcement Learning. 
520    These 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: evolving developmental programs for neural 
       networks solving multiple problems, tangled program, 
       transfer learning and outlier detection using GP, program 
       search for machine learning pipelines in reinforcement 
       learning, automatic programming with GP, new variants of 
       GP, like SignalGP, variants of lexicase selection, and 
       symbolic regression and classification techniques. The 
       volume includes several chapters on best practices and 
       lessons learned from hands-on experience. 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 Artificial intelligence 
650  0 Computational intelligence 
650  0 Algorithms 
700 1  Banzhaf, Wolfgang 
700 1  Spector, Lee 
700 1  Sheneman, Leigh 
830  0 Genetic and Evolutionary Computation,|x1932-0167