Genetic programming and emergent intelligence
Advances in genetic programming
Studying artificial life using a simple, general cellular model
Artificial Life
Computational mechanics of cellular automata: an example
Proceedings of the workshop on Lattice dynamics
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets, Fuzzy Sets and Knowledge Discovery
Rough Sets, Fuzzy Sets and Knowledge Discovery
An agent model for rough classifiers
Applied Soft Computing
Rough sets for adapting wavelet neural networks as a new classifier system
Applied Intelligence
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Many systems in nature produce complicated behaviors,which emerge from the local interactions of relatively simple individual components that live in some spatially extended world. Notably, this type of emergent behavior formation often occurs without the existence of a central control. The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. This paper introduces the emergent computational paradigm and discusses its applicability and potential in rough sets theory. In emergence algorithm, the overall system dynamics emerge from the local interactions of independent objects or agents. For accepting a system is displaying an emergent behavior, the system should be constructed by describing local elementary interactions between components in different ways than those used in describing global behavior and properties of the running system over a period of time. The proposals of an emergent computation structure for implementing basic rough sets theory operators are also given in this paper.