Genetic Programming Theory and Practice XI (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.
epidemiological studies (Moore and Williams 2009). Several recent studies have highlighted the importance of gene-gene interactions in Alzheimer’s disease and thus provide a foundation for further investigation using data mining and machine learning methods that are ideally suited to detecting nonlinear effects of attribute combinations (Combarros et al. 2009; Lehmann et al. 2012; Bullock et al. 2013). Although promising, these studies only explored pairwise gene-gene interactions. The search for
Chiang FT, Holden T, Barney N, White BC (2006) A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 241(2):252–261. doi:10.1016/j.jtbi.2005.11.036, http://dx.doi.org/10.1016/j.jtbi.2005.11.036 Moore JH, Andrews PC, Barney N, White BC (2008) Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility
the application of purchasing cloud computing services. We showed that the most successful strategy for selecting contract portfolios is based on upfront reservation of compute instances using forecasted utilization levels obtained by genetic programming. We showed that symbolic regression via genetic programming (implemented in DataModeler) routinely filters out handfuls of driving variables out of several hundreds of candidate inputs. We also demonstrated the competitive advantage of running
practice to limit the maximum depth of a GP individual to some manageable limit at the start of a symbolic regression run. Given any selected maximum depth k, it is an easy process to construct a maximal binary tree s-expression U k , which can be produced by the GP system without violating the selected maximum depth limit. As long as we are reminded that each f represents a function node while each t represents a terminal node, the construction algorithm is simple and recursive as follows. (U
We did this, essentially, by ignoring the syntactic structure of programs during the first phase of the action of the operator. This “syntax blindness” can produce children that violate syntactic constraints, so we must follow the syntax-blind variation step with a repair step that ensures or restores syntactic validity. While we do not claim that our new operator is “perfectly” uniform in the sense that we are using that term, we do believe that it is more uniform than other operators described