Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?

Abstract : Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far. Our elitist model thus combines features of the ranking-based and the memoryrestricted black-box models with an enforced usage of truncation selection.
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https://hal.sorbonne-universite.fr/hal-01379099
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Submitted on : Tuesday, October 11, 2016 - 10:10:56 AM
Last modification on : Friday, March 22, 2019 - 1:43:56 AM

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Carola Doerr, Johannes Lengler. Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?. Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2016, ⟨10.1162/EVCO_a_00195⟩. ⟨hal-01379099⟩

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