Fitness landscape analysis and visualisation can provide significant insights into problem instances and algorithm behaviour. The aim of the workshop is to encourage and promote the use of landscape analysis to improve the understanding, the design and, eventually, the performance of search algorithms. Examples include landscape analysis as a tool to inform the design of algorithms, landscape metrics for online adaptation of search strategies, mining landscape information to predict instance hardness and algorithm runtime.
The workshop will focus on, but not be limited to, topics such as:
Evolvability and searchability characterisation
Exploiting problem structure
Exploratory landscape analysis
Fitness landscape analysis
Fitness landscape visualisation
Fitness landscape theory
Grey-box optimisation
Informed search strategies
Local optima networks
Multi-objective fitness landscapes
Performance and failure prediction
The first edition of this workshop was hosted at PPSN 2016.
Workshop date: 16 July 2017
Time: 08:30 — 10:20
Location: Andel's by Vienna House Hotel, Berlin
08:30 – 10:20
Exploiting Active Subspaces in Global Optimization: How Complex is your Problem?
Pramudita Palar and Koji Shimoyama
The Effect of Landscape Funnels in QAPLIB Instances
Sarah L. Thomson, Gabriela Ochoa, Fabio Daolio and Nadarajen Veerapen
Instance-Based Algorithm Selection on Quadratic Assignment Problem Landscapes
Andreas Beham, Michael Affenzeller and Stefan Wagner
Analyzing Deception, Evolvability, and Behavioral Rarity in Evolutionary Robotics
Joel Lehman
Long-Term Stability of Genetic Programming Landscapes
William Langdon
Nadarajen Veerapen, Arnaud Liefooghe, Sébastien Verel and Gabriela Ochoa
Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Stirling, FK9 4LA, Scotland.
Université Lille 1, LIFL – CNRS, 59655 Villeneuve d’Ascq Cedex, France.
Inria Lille-Nord Europe, 59650 Villeneuve d'Ascq, France.
Université du Littoral Côte d'Opale, LISIC, 62228 Calais, France.