Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Natural Computing: an international journal
Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach
IEEE Computational Intelligence Magazine
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The talk presents theoretical foundations and practical applications of evolving intelligent information processing systems inspired by information principles in Nature in their interaction and integration. That includes neuronal-, genetic-, and quantum information principles, all manifesting the feature of evolvability. First, the talk reviews the main principles of information processing at neuronal-, genetic-, and quantum information levels. Each of these levels has already inspired the creation of efficient computational models that incrementally evolve their structure and functionality from incoming data and through interaction with the environment. The talk also extends these paradigms with novel methods and systems that integrate these principles. Examples of such models are: evolving spiking neural networks; computational neurogenetic models (where interaction between genes, either artificial or real, is part of the neuronal information processing); quantum inspired evolutionary algorithms; probabilistic spiking neural networks utilizing quantum computation as a probability theory. The new models are significantly faster in feature selection and learning and can be applied to solving efficiently complex biological and engineering problems for adaptive, incremental learning and knowledge discovery in large dimensional spaces and in a new environment. Examples include: incremental learning systems; on-line multimodal audiovisual information processing; evolving neuro-genetic systems; bioinformatics; biomedical decision support systems; cyber-security. Open questions, challenges and directions for further research are presented.