HFC: A Continuing EA Framework for Scalable Evolutionary Synthesis

Juinjun Hu, Eric D. Goodman, Kisung Seo, Zhun Fan, and Ronald Rosenberg

The scalability of evolutionary synthesis is impeded by its characteristic discrete landscape with high multimodality. It is also impaired by the convergent nature of conventional EAs. A generic framework, called Hierarchical Fair Competition (HFC), is proposed for formulation of continuing evolutionary algorithms. This framework features a hierarchical organization of individuals by different fitness levels. By maintaining repositories of intermediate-fitness individuals and ensuring a continuous supply of raw genetic material into an environment in which it can be exploited, HFC is able to transform the convergent nature of current EAs into a sustainable evolutionary search framework. It is also well suited for the special demands of scalable evolutionary synthesis. An analog circuit synthesis problem, the eigenvalue placement problem, is used as an illustrative case study.


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