EvoKNOW 2018 - Knowledge Incorporation in Evolutionary Computation

This track, organized under the aegis of the H2020 FET-OPEN project "PHOENIX: Exploring the Unknown through Reincarnation and Co- evolution", aims at bringing together researchers and practitioners from all application domains dealing with the incorporation of knowledge into evolutionary techniques. We seek high-quality contributions focused on multi-disciplinary approaches combining Evolutionary Computation, knowledge representation, knowledge transfer, case-based reasoning, and real-world applications thereof. Topics of interest include but are not limited to:
Incorporation of domain knowledge into evolutionary algorithms
Novel techniques to identify and elicit explicit and/or implicit expert knowledge relevant to evolutionary algorithms
Innovative knowledge representation strategies for Evolutionary Computation
Case-based reasoning applied to evolutionary algorithms
Knowledge transfer across applications and domains
Methods to account for uncertainties and contradictions in knowledge
Knowledge-informed surrogate-assisted evolutionary algorithms
Lifetime learning and Interactive Evolutionary Computation
Memetic computing and domain-specific evolutionary techniques
Real-world applications of knowledge-based Evolutionary Computation