TY - CONF

T1 - Evolving computational intelligence systems

AU - Angelov, Plamen

AU - Kasabov, N

PY - 2005/3

Y1 - 2005/3

N2 - A new generation of computational intelligent systems is introduced in a generic framework of the evolving computational intelligence systems (ECIS) that develop, unfold their structure and functionality from incoming data. ECIS constitute a suitable paradigm for adaptive modeling of continuous dynamic processes and tracing the evolution of knowledge. The elements of evolution, such as inheritance and structure development are related to the knowledge and data pattern dynamics and are considered in the context of an individual system/model. Although this concept differs from the concept of evolutionary (genetic) computing, both paradigms heavily borrow from the same source – nature and human evolution. As the origin of knowledge, humans are the best model of an evolving intelligent system. Instead of considering the evolution of population of spices or genes as the evolutionary computation algorithms does the ECIS concentrate on the evolution of one specific intelligent system. The aim is to develop the intelligence/knowledge of this system through an evolution using inheritance and modification, upgrade and reduction. This approach is also suitable for the integration of new data and existing models into new models that can be incrementally adapted to future incoming data. This powerful new concept has been recently introduced by the authors in a series of works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. Two basic approaches, namely ECOS and EFS are referred as working examples of ECIS. The ideas are supported by several illustrative examples.

AB - A new generation of computational intelligent systems is introduced in a generic framework of the evolving computational intelligence systems (ECIS) that develop, unfold their structure and functionality from incoming data. ECIS constitute a suitable paradigm for adaptive modeling of continuous dynamic processes and tracing the evolution of knowledge. The elements of evolution, such as inheritance and structure development are related to the knowledge and data pattern dynamics and are considered in the context of an individual system/model. Although this concept differs from the concept of evolutionary (genetic) computing, both paradigms heavily borrow from the same source – nature and human evolution. As the origin of knowledge, humans are the best model of an evolving intelligent system. Instead of considering the evolution of population of spices or genes as the evolutionary computation algorithms does the ECIS concentrate on the evolution of one specific intelligent system. The aim is to develop the intelligence/knowledge of this system through an evolution using inheritance and modification, upgrade and reduction. This approach is also suitable for the integration of new data and existing models into new models that can be incrementally adapted to future incoming data. This powerful new concept has been recently introduced by the authors in a series of works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. Two basic approaches, namely ECOS and EFS are referred as working examples of ECIS. The ideas are supported by several illustrative examples.

M3 - Conference paper

SP - 76

EP - 82

T2 - 1st International Workshop on Genetic Fuzzy Systems

Y2 - 17 March 2005 through 19 March 2005

ER -