Feeding Genetic Heterogeneity via a Smart Mutation Operator in the Memetic Phase Retrieval Approach

  • Marta Mauri
  • Davide Emilio Galli
  • Alessandro ColomboEmail author
Conference paper


A memetic algorithm is a stochastic optimization method obtained by hybridizing an evolutionary approach with common deterministic optimization procedures. The recently introduced Memetic Phase Retrieval (MPR) approach exploits this synergy to face the so-called phase retrieval problem in Coherent Diffraction Imaging (CDI). Here we focus on the development of a smart mutation genetic operator; our aim is the improvement of MPR performance by continually feeding with relevant information the genetic heritage of the population of candidate solutions. Remarkably, statistical tests on synthetic CDI data performed using MPR enhanced via a smart mutation operator reveal a smaller reconstruction error with respect to an MPR implementation supplied with a blind random mutation only.


Coherent diffraction imaging Memetic algorithms Phase retrieval problem Computational intelligence 



We acknowledge L. De Caro, E. Carlino and F. Scattarella for useful discussions. This work was supported by the NOXSS PRIN (2012Z3N9R9) project. We acknowledge the CINECA and Regione Lombardia LISA award LI05p-PUMAS, the CINECA ISCRA–C award IMAGES and the CINECA ISCRA–B award MEMETICO for the availability of high performance computing resources and support.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marta Mauri
    • 1
  • Davide Emilio Galli
    • 1
  • Alessandro Colombo
    • 1
    Email author
  1. 1.Dipartimento di Fisica “Aldo Pontremoli”Università degli Studi di MilanoMilanItaly

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