Presenting the ECO: Evolutionary Computation Ontology

A well-established notion in Evolutionary Computation (EC)is the importance of the balance between exploration and exploitation.Data structures (e.g. for solution encoding), evolutionary operators, se-lection and fitness evaluation facilitate this balance. Furthermore, theability of an Evolutionary Algorithm (EA) to provide efficient solutionstypically depends on the specific type of problem. In order to obtainthe most efficient search, it is often needed to incorporate any availableknowledge (both at algorithmic and domain level) into the EA. In thiswork, we develop an ontology to formally represent knowledge in EAs.Our approach makes use of knowledge in the EC literature, and can beused for suggesting efficient strategies for solving problems by means ofEC. We call our ontology “Evolutionary Computation Ontology” (ECO).In this contribution, we show one possible use of it, i.e. to establish a linkbetween algorithm settings and problem types. We also show that theECO can be used as an alternative to the available parameter selectionmethods and as a supporting tool for algorithmic design