• An aggregation approach for solving the non-linear fractional equality Knapsack problem 

      Yazidi, Anis; Jonassen, Tore Møller; Herrera-Viedma, Enrique (Expert systems with applications;Volume 110, 15 November 2018, Journal article; Peer reviewed, 2018-06-15)
      In this paper, we present an optimal, efficient and yet simple solution to a class of the deterministic non-linear fractional equality knapsack (NEFK) problem — a substantial resource allocation problem. The solution is shown ...
    • Distributed Learning Automata-based S-learning scheme for classification 

      Goodwin, Morten; Yazidi, Anis; Jonassen, Tore Møller (Pattern Analysis and Applications;Published online 12 October 2019, Journal article; Peer reviewed, 2019-09-10)
      This paper proposes a novel classifier based on the theory of Learning Automata (LA), reckoned to as PolyLA. The essence of our scheme is to search for a separator in the feature space by imposing an LA-based random walk ...
    • Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation 

      Yazidi, Anis; Hammer, Hugo Lewi; Jonassen, Tore Møller (Applied Intelligence;Volume 49, Issue 9, Journal article; Peer reviewed, 2019-04-11)
      The Stochastic Non-linear Fractional Equality Knapsack (NFEK) problem is a substantial resource allocation problem which admits a large set of applications such as web polling under polling constraints, and constrained ...
    • Two-timescale learning automata for solving stochastic nonlinear resource allocation problems 

      Yazidi, Anis; Hammer, Hugo Lewi; Jonassen, Tore Møller (Lecture Notes in Computer Science;LNCS, volume 10350, Journal article; Peer reviewed, 2017)
      This papers deals with the the Stochastic Non-linear Fractional Equality Knapsack (NFEK) problem which is a fundamental resource allocation problem based on incomplete and noisy information [2,3]. The NFEK problem arises ...