id summary reporter owner description type status priority milestone component version resolution keywords cc 1683 Semantic similarity crossover bburlacu bburlacu "This type of crossover works under the assumption that the exchange of subtrees is most likely to be beneficial if they are not semantically identical, but also not too different. The semantic similarity of two subtrees is determined by comparing them on a set of points in the solution space and taking the average absolute difference as a similarity measure. This measure is denoted ''sampling semantic distance'' (SSD) and it depends on the number of points as well as on the strategy of choosing those points (random or heuristic-based). Two trees ''S,,1,,'' and ''S,,2,,'' are considered ''similar'' if SSD(S,,1,,,S,,2,,) lies within the bounds of a so-called ''syntactic sensitivity'' interval between two predefined constants. In general, the best values for these semantic sensitivity bounds are problem dependent. In their papers, [''1''], [''2''], ''Uy et al.'' show that improving semantic locality significantly improves GP performance, reduces code bloat, and substantially enhances generalization. References: [''1''] [http://dl.acm.org/citation.cfm?id=1883745 Semantic similarity based crossover in GP: the case for real-valued function regression], EA'09 Proceedings of the 9th international conference on Artificial Evolution [''2''] [http://dl.acm.org/citation.cfm?id=1887313&CFID=68725921&CFTOKEN=77399060 The role of syntactic and semantic locality of crossover in genetic programming], PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II" feature request closed medium HeuristicLab 3.3.7 Problems.DataAnalysis.Symbolic 3.3.7 done mkommend