Genetic Programming Theory and Practice X (Genetic and Evolutionary Computation)
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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: 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.
Role as parent for its growth is very much appreciated by all of the attendees over the years. Thanks Rick! Milan, Michigan Bill Worzel Bill Worzel Milan, Michigan Preface Preface The work described in this book was first presented at the Tenth Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, May 12–14, 2012. The goal of this workshop series is to promote the exchange of research results.
Individuals at a given time – i.e. the solution. The Evolution Coordinator uses the Archive as a reservoir of individuals to be sent out to each Evolution Engine in order to improve fitness and possibly mix their genetic material with the endemic population that evolves on each Evolution Engine. Individuals are stored in the Evolution Coordinator Archive in layers. The number of layers and individuals per layer is fixed. When a new or returning individual comes to the Evolution Coordinator, first.
That the incorporation of greater biological realism into GP may improve its ability to solve complex, real-world problems. Specifically, (Banzhaf et al., 2006) have called for the development of open-ended computational evolutionsystems (CES) that attempt to emulate, rather than ignore, the complexities of biotic systems. With this in mind, we have recently developed a hierarchical, spatially-explicit CES that allows for the evolution of arbitrarily complex solutions and solution operators, and.
Prognostics, games, control algorithms design, stock trading, life sciences, and insurance law. An important change this year compared with previous workshops was a more varied mix of different representations of GP individuals in presented systems. We made a coordinated effort to expand the topics of practical applications of GP far beyond GP symbolic regression for data fitting, and we think we achieved success. Important topics in general purpose GP were the focus of many papers this year:.
Matured significantly in the last few years. There is at least one commercial package on the market for several years http://www.rmltech.com/. There is now at least one well documented commercial symbolic regression package available for Mathematica www.evolved-analytics.com. There is at least one very well done open source symbolic regression package available for free download http://ccsl.mae.cornell.edu/eureqa. In addition to our own ARC system (Korns, 2010), currently used internally for.