@inproceedings{a5dff4ec4830466aade0b9faf1ff2e8b,
title = "On the theoretical capacity of evolution strategies to statistically learn the landscape",
abstract = "We investigate the covariance matrix when constructed by Evolution Strategies (ESs) operating with the selection operator alone. We model continuous generation of candidate solutions about quadratic basins of attraction, with deterministic selection of the decision vectors that minimize the objective function values. Our goal is to rigorously show that accumulation of winning individuals carries the potential to reveal valuable information about the search landscape. We first show that the statistically-constructed covariance matrix over such winning decision vectors shares the same eigenvectors with the Hessian matrix about the optimum. We then provide an analytic approximation of this covariance matrix for a non-elitist multi-child (1,λ)-strategy, which holds for a large population size λ.",
keywords = "Covariance matrix adaptation, Extreme value distributions, Landscape hessian, Limit distributions of order statistics, Statistical learning, Theory of evolution strategies",
author = "Shir, {Ofer M.} and Jonathan Roslund and Amir Yehudayoff",
note = "Publisher Copyright: {\textcopyright} 2016 Copyright held by the owner/author(s).; 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion ; Conference date: 20-07-2016 Through 24-07-2016",
year = "2016",
month = jul,
day = "20",
doi = "10.1145/2908961.2909057",
language = "الإنجليزيّة",
series = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
pages = "151--152",
editor = "Tobias Friedrich",
booktitle = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
}