Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Compaction for code fragment based learning classifier systems - Redux

Learning Classifier Systems (LCSs) originated from artificial cognitive systems, eventually they migrated such that LCS became powerful classification techniques. Current LCSs can extract building blocks of knowledge utilizing Code Fragments in order to scale to more difficult problems in the same or a related domain. Code Fragments (CF) are GP-like sub-trees where past learning can be reused in their leaf or root nodes. A downside is that the expressive alphabet used by the CFs requires more computing resources as the learned knowledge grows. The long chains of CFs that eventually appear make CFs incapable of scaling to more complex problems. Previous work shows that a new layer of Distilled Rules (DRs) created in batch mode after training, was beneficial in future reuse, but at the cost of long computational times. In the novel work here, an innovative online method to produce DRs is described and compared with the original method. The system has been tested on Boolean problems up to the 70 bit multiplexer and 3×11 bit hidden multiplexer, which are difficult problems for conventional algorithms. This is due to the large search spaces involved. The new technique has been shown to create a new layer of DRs for the 70 Mux, something that the previous version was unable to accomplish in a timely manner. It has also been able to scale to more difficult problems in the same or a related domain.</p