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
Previous editions of the workshop:
Workshop date: 15 July 2018
Time: 14:50 — 16:30
Location: Kyoto Terrsa, Conference Room B (3F)
14:50 – 16:30
Progressive Gradient Walk for Neural Network Fitness Landscape Analysis
Anna Sergeevna Bosman, Andries Engelbrecht, Marde Helbig
Computationally Efficient Local Optima Network Construction
Jonathan E. Fieldsend
Filter versus Wrapper Feature Selection based on Problem Landscape Features
Werner Mostert, Katherine Malan, Andries Engelbrecht
Discussion
Suggested topic: Challenges and Future Directions for Landscape-Aware Heuristics Search
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 de Lille, CRIStAL – 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.