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    Hybrid Selection Allows Steady-State Evolutionary Algorithms to Control the Selective Pressure in Multimodal Optimisation
    (Assoc Computing Machinery, 2025) Corus, Dogan; Oliveto, Pietro S.; Zheng, Feiyang
    Recent work has shown that Inverse Tournament Selection operators within steady-state evolutionary algorithms (EAs) allow to control the selective pressure much more accurately than in generational EAs. However, to achieve low selective pressures, large tournament sizes are required which come at the cost of prohibitive expected times for the population to escape from local optima. To this end, we propose a hybrid selection mechanism that leads to considerable speed-ups in the expected time to escape from local optima while permitting to keep the selective pressure arbitrarily low and the use of large population sizes. The mechanism simply switches between Inverse Elitist selection and Uniform selection when it detects that the population is stuck on local optima, and switches back when an improving solution is found. We prove its effectiveness for the TruncatedTwomax.. and RidgeWithBranches.. benchmarks from the literature by providing super-linear speed-ups over the (.. +1) EA with any fixed selective pressure.