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Symbolic regression via genetic programming (hereafter, referred to simply as symbolic regression) has proven to be a very important tool for industrial empirical modeling (Kotanchek et al., 2003). Two of the primary problems with industrial use of symbolic regression are (1) the relatively large computational demands in comparison with other nonlinear empirical modeling techniques such as neural...
In this chapter we illustrate a framework based on symbolic regression to generate and sharpen the questions about the nature of the underlying system and provide additional context and understanding based on multi-variate numeric data. We emphasize the necessity to perform data modeling in a global approach, iteratively applying data analysis and adaptation, model building, and problem reduction...
The future of computing is one of massive parallelism. To exploit this and generatemaximumperformance itwill be inevitable thatmore co-design between hardware and software takes place. Many software algorithms need rethinking to expose all the possible concurrency, increase locality and have built-in fault tolerance. Evolutionary algorithms are naturally parallel and should as such have an edge in...
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