Individuals encoding potential rules to model an actual partition of samples into categories may be evolved by means of several well-known evolutionary classification techniques. Nevertheless, since a canonical evolutionary algorithm progresses towards one (global or local) optimum, some special construction or certain additional method are designed and attached to the classifier in order to maintain several basins of attraction of the different prospective rules. With the aim of offering a simpler option to these complex approaches and with an inspiration from the state-of-the-art cooperative coevolutionary algorithms, this chapter presents a novel classification tool, where rules for each class are evolved by a distinct population. Prototypes evolve simultaneously while they collaborate towards the goal of a good separation, in terms of performance and generalization ability. A supplementary archiving mechanism, which preserves a variety of the best evolved rules and eventually yields a thorough and diverse rule set, increases the forecasting precision of proposed technique. The novel algorithm is tested against two real-world decision problems regarding tumor diagnosis and obtained results demonstrate the initial presumption.
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- Evolution of Cooperating Classification Rules with an Archiving Strategy to Underpin Collaboration
- Springer Berlin Heidelberg
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