Artificial life: the quest for a new creation
Artificial life: the quest for a new creation
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Simple Approach to Digital Signal Processing
A Simple Approach to Digital Signal Processing
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Hi-index | 0.00 |
The similarity assessment process often involves measuring the similarity of objects X and Y in terms of the similarity of corresponding constituents of X and Y, possibly in a recursive manner. This approach is not useful when the verbatim value of the data is of less interest than what they can potentially "do," or where the objects of interest have incomparable representations. We consider the possibility that objects can have behavior independent of their representation, and so two objects can look similar, but behave differently, or look quite different and behave the same. This is of practical use in fields such as Artificial Life and Automatic Code Generation, where behavior is considered the ultimate determining factor. It is also useful when comparing objects that are represented in different forms and are not directly comparable. We propose to map behavior into data values as a preprocessing step to Rough Set methods. These data values are then treated as normal attributes in the similarity assessment process.