Special Session

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Fitness Landscape Analysis for Understanding and Designing Intelligent Optimization Algorithms

Overview

Evolutionary algorithms and other classes of meta-heuristics are often used to solve computationally hard single-/multi-objective continuous/combinatorial optimization problems. Such randomized search heuristics include evolutionary algorithms, neighborhood-based search, simulated annealing, tabu search, iterated local search, evolution strategy, memetic algorithms, hyper-heuristics, etc. Successful applications of evolutionary computation can be found in fields like engineering, scheduling, timetabling, planning, network design, transportation and distribution problems, vehicle routing, traveling salesman, packing, power systems, image processing, among many others.

Scope

This special session aims at bringing together researchers working on understanding the relations between the problem structure and the algorithm performance.

The aim of this special session is to cover the whole spectrum of fitness landscapes and algorithm design, including:

  • Understanding problem difficulty and algorithm complexity
  • Problem structure analysis, fundamental search space properties
  • Gain problem knowledge and learn about the problem structure
  • Metrics and descriptors for describing problem features
  • Estimating problem features for large-size problem instances
  • Characterizing local optimality, ruggedness and neutrality
  • Graphs and complex networks for modeling fitness landscapes
  • Theory and applications of elementary landscapes
  • Algorithm behavior and search performance
  • Choice for representation, evaluation, neighborhood structures and variation operators
  • Feature-based performance analysis, correlation and regression
  • Fitness landscape analysis for the configuration and selection of algorithms
  • Automated off-line/tuning and on-line/control/adaptation of parameters, hyper-heuristics
  • Performance prediction, performance robustness and scalability issues in the search space (large-scale optimization) and in the objective space (many-objective optimization)
  • Software and visualization tools for fitness landscape analysis
  • Benchmarking, construction of new models and test problems, problem taxonomies
  • Fitness landscape analysis for problems with continuous, combinatorial, and mixed variables
  • Fitness landscape analysis for single and multi-objective optimization
  • Fitness landscape analysis of real-world applications

Paper submission

Papers submitted to the special session will be treated as regular papers, and accepted papers will be included in the conference proceedings. Papers should be prepared according to the format and page limit of regular papers specified for CEC 2017. When submitting your paper, make sure to select the special session name from the main research topic list. Papers shall be submitted through the official CEC 2017 website.

Important dates

  • Deadline for paper submission: January 16, 2017 January 30, 2017
  • Notification of acceptance: February 26, 2017
  • Conference dates: June 5-8, 2017

Organizers

  • Hernan Aguirre — Shinshu University (JP) — ahernan [at] shinshu-u.ac.jp
  • Arnaud Liefooghe — Univ. Lille (FR) — arnaud.liefooghe [at] univ-lille1.fr
  • Kiyoshi Tanaka — Shinshu University (JP) — ktanaka [at] shinshu-u.ac.jp
  • Sébastien Verel — Univ. Littoral Côte d'Opale (FR) — verel [at] lisic.univ-littoral.fr

Past events