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.
DOI doi:10.1007/978-3-642-29178-4-42 Gustafson S, Burke EK (2006) The speciating island model: An alternative parallel evolutionary algorithm. Journal of Parallel and Distributed Computing 66(8):1025–1036, DOI doi:10.1016/j.jpdc.2006.04.017, parallel Bioinspired Algorithms Harper R (2010) Spatial co-evolution in age layered planes (SCALP). In: CEC, IEEE Jelasity M, Preuß M, Van Steen M, Paechter B (2002) Maintaining connectivity in a scalable and robust distributed environment. In: 2nd
are more consistent, with an optimum at γ = 2. 0, respectively γ = 3. 0. Statistical test show that most important number of pairs are, indeed, statistically different. Discussion, Conclusions, and Future Work Although this is preliminary exploratory work, we demonstrate that in all problems, all values of γ in the Lévy flight mutation show higher performance than the constant mutation rate that is usually used in Genetic Programming. We believe the behaviors observed in these experiments are
symbolic regression with swarm intelligence algorithms specifically designed to evolve real constants. Abstract Constants In standard Koza-style tree-based Genetic Programming (Koza, 1992) the genome and the individual are the same Lisp s-expression which is usually illustrated as a tree. Of course the tree-view of an s-expression is only a visual aid, since a Lisp s-expression is normally a list which is a special Lisp data structure. Without altering or restricting standard tree-based GP in
Analytics and Jason Moore of theComputational Genetics Laboratory of Dartmouth College. We would like to thank all participants for another wonderful workshop. We believe GPTP do bring a systematic approach to understanding and advancing GP in theory and practice and look forward to the GPTP-2013. Acknowledgments We thank all the workshop participants for making the workshop an exciting and productive 3 days. In particular we thank the authors, without whose hard work and creative talents,
heuristics) at different phases of the game, with a phase defined by , where g is the number of moves made so far, and h is the value of the original heuristic. For example, suppose 10 moves have been made (g = 10), and the value returned by LowestFoundationCard is 5. The PhaseByLowestFoundationCard heuristic will return or 2 ∕ 3 in this case, a value that represents the belief that by using this heuristic the configuration being examined is at approximately 2 ∕ 3 of the way from the initial