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A role-based imitation algorithm for the optimisation in dynamic fitness landscapes

A role-based imitation algorithm for the optimisation in dynamic fitness landscapes,10.1109/SIS.2011.5952571,Emre Cakar,Sven Tomforde,Christian Muller

A role-based imitation algorithm for the optimisation in dynamic fitness landscapes   (Citations: 2)
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    • ...According to Cakar et al. [23], systems can be classified into three categories considering the time-dependent character of the learning problem’s fitness landscape: static, dynamic, and self-referential...
    • ...Future work will focus on extending the scope of the implementation by investigating the more complex classes of “dynamic” and “self-referential” fitness landscapes according to Cakar’s classification [23]...

    Sven Tomfordeet al. Restricted on-line learning in real-world systems

    • ...For the comparison in static fitness landscapes, we used different benchmark functions from the literature, where each algorithm tries to find the global minimum in the given fitness landscape [3]...
    • ...Overall, only RBI and PSO can cope with the increasing complexity in this scenario, while RBI outperforms all its competitors [3]...

    Emre Cakaret al. Aspects of Learning in OC Systems

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