Alexander Melkozerov

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This paper presents a performance comparison of 4 direct search strategies in continuous search spaces using the noisy sphere as test function. While the results of the Evolution Strategy (ES), Evolutionary Gradient Search (EGS), Simultaneous Perturbation Stochastic Approximation (SPSA) considered are already known from literature, Implicit Filtering (IF)(More)
This paper presents a performance analysis of the recently proposed σ-self-adaptive weighted recombination evolution strategy (ES) with scaled weights. The steady state behavior of this ES is investigated for the non-noisy and noisy case, and formulas for the optimal choice of the learning parameter are derived allowing the strategy to reach maximal(More)
Cigar functions are convex quadratic functions that are characterised by the presence of only two distinct eigenvalues of their Hessian, the smaller one of which occurs with multiplicity one. Their ridge-like topology makes them a useful test case for optimisation strategies. This paper extends previous work on modelling the behaviour of evolution(More)
—The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multi-recombination (the (µ/µI , λ)-σSA-ES) using isotropic mutations is investigated on convex-quadratic functions (referred to as ellipsoid model). An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES(More)
In this paper, first results on the analysis of self-adaptive evolution strategies (ES) with intermediate multirecombination on the elliptic model are presented. Equations describing the ES behavior on the ellipsoid will be derived using a deterministic approach and experimentally verified. A relationship between newly obtained formulae for the elliptic(More)
This paper analyzes the multi-recombinant self-adaptive evolution strategy (ES), denoted as(&#956;/&#956;<sub>I</sub>, &#955;)-&#963;SA-ES on the convex-quadratic function class under the influence of noise, which is referred to as noisy ellipsoid model. Asymptotically exact progress rate and self-adaptation response measures are derived (i.e., for <i>N</i>(More)
This paper proposes the <i>&#963;-self-adaptive weighted multirecombination evolution strategy</i> (ES) and presents a performance analysis of this newly engineered ES. The steady state behavior of this strategy is investigated on the sphere model and a formula for the optimal choice of the learning parameter is derived allowing the ES to reach maximal(More)
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