---
res:
  bibo_abstract:
  - Artificial benchmark functions are commonly used in optimization research because
    of their ability to rapidly evaluate potential solutions, making them a preferred
    substitute for real-world problems. However, these benchmark functions have faced
    criticism for their limited resemblance to real-world problems. In response, recent
    research has focused on automatically generating new benchmark functions for areas
    where established test suites are inadequate. These approaches have limitations,
    such as the difficulty of generating new benchmark functions that exhibit exploratory
    landscape analysis (ELA) features beyond those of existing benchmarks.The objective
    of this work is to develop a method for generating benchmark functions for single-objective
    continuous optimization with user-specified structural properties. Specifically,
    we aim to demonstrate a proof of concept for a method that uses an ELA feature
    vector to specify these properties in advance. To achieve this, we begin by generating
    a random sample of decision space variables and objective values. We then adjust
    the objective values using CMA-ES until the corresponding features of our new
    problem match the predefined ELA features within a specified threshold. By iteratively
    transforming the landscape in this way, we ensure that the resulting function
    exhibits the desired properties. To create the final function, we use the resulting
    point cloud as training data for a simple neural network that produces a function
    exhibiting the target ELA features. We demonstrate the effectiveness of this approach
    by replicating the existing functions of the well-known BBOB suite and creating
    new functions with ELA feature values that are not present in BBOB.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Raphael Patrick
      foaf_name: Prager, Raphael Patrick
      foaf_surname: Prager
  - foaf_Person:
      foaf_givenName: Konstantin
      foaf_name: Dietrich, Konstantin
      foaf_surname: Dietrich
  - foaf_Person:
      foaf_givenName: Lennart
      foaf_name: Schneider, Lennart
      foaf_surname: Schneider
  - foaf_Person:
      foaf_givenName: Lennart
      foaf_name: Schäpermeier, Lennart
      foaf_surname: Schäpermeier
  - foaf_Person:
      foaf_givenName: Bernd
      foaf_name: Bischl, Bernd
      foaf_surname: Bischl
  - foaf_Person:
      foaf_givenName: Pascal
      foaf_name: Kerschke, Pascal
      foaf_surname: Kerschke
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  - foaf_Person:
      foaf_givenName: Olaf
      foaf_name: Mersmann, Olaf
      foaf_surname: Mersmann
  bibo_doi: 10.1145/3594805.3607136
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9798400702020
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_subject:
  - Benchmarking
  - Instance Generator
  - Black-Box Continuous Optimization
  - Exploratory Landscape Analysis
  - Neural Networks
  dct_title: Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory
    Landscape Features@
...