Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms? - Archive ouverte HAL Access content directly
Journal Articles Evolutionary Computation Year : 2017

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.

Dates and versions

hal-01379099 , version 1 (11-10-2016)

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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, 2017, 25 (4), pp.587 - 606. ⟨10.1162/EVCO_a_00195⟩. ⟨hal-01379099⟩
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