This WCCI 2020 special session is organized in association with the IEEE Computational Intelligence Society’s Technical Committee on Bioinformatics and Bioengineering (BBTC).
In global optimization techniques, it is often the case that the data structures used to represent a candidate solution can be directly decoded and interpreted; hence, the best fitting individual immediately provides an explicit and human-readable description of the optimal solution. Research mainly focused on the creation of novel strategies, aimed at balancing the exploration and exploitation capabilities of optimization algorithms.
Although this well established research line is very prolific—paving the way to the design of efficient algorithms even for large-scale problems—there is another promising direction that could be investigated, consisting in the modification of the search space, that is, space transformation able to dilate, shrink, stretch, collapse, or remap the fitness landscape, leading to alternative or simplified formulations of the original optimization problem.
In discrete domains, a similar approach can be performed by embedding implicit or explicit assumptions into the structure of candidate solutions, so that genetic operators can explore the feasible search space in a “smarter” way, reducing the overall computational effort. Example of this technique include generative representations, techniques based on grammars, single parent techniques where example genes are incorporated, and other structured representations that starkly limit the portion of the search domain examined.
This special session aims at gathering the researchers who are investigating new directions and ideas in the field of candidate solutions representation and its dual notion fitness landscape manipulation.
This Special Session welcomes any paper considering all kinds of non-conventional candidate solutions representation, including the dual perspective of fitness landscape manipulation.
Examples include but are not limited to:
- Non-conventional representations of candidate solutions
- Dilation functions and other functions that reshape the fitness landscape
- Alternative semantics for candidate solutions
- Fitness landscape modification, simplification, and restriction
- Novel closed variation/evolutionary operators
- Implicit/relative representations
- Generative or developmental representations
- Self-adaptive representations
- Parameterized manifolds of representations
- State-conditioned representations
- Procedural representations
- Generative automata
- Surrogate models
- Wendy Ashlock (Ashlock and McGuinness Consulting, Canada)
- Daniela Besozzi (University of Milano-Bicocca, Italy)
- Joseph A. Brown (Innopolis University, Russia)
- Paolo Cazzaniga (University of Bergamo, Italy)
- Caro Fuchs (Eindhoven University of Technology, The Netherlands)
- Ivo Gonçalves (University of Coimbra, Portugal)
- James Hughes (St. Francis Xavier University, NS, Canada)
- Sheridan Houghten (Brock University, ON, Canada)
- Uzay Kaymak (Eindhoven University of Technology, The Netherlands)
- Luca Manzoni (University of Trieste, Italy)
- Eric Medvet (University of Trieste, Italy)
- Alberto Moraglio (University of Exeter, UK)
- Giancarlo Mauri (University of Milano-Bicocca, Italy)
- Gonzalo Ruz (Universidad Adolfo Ibanez, Chile)
- Sara Silva (Universidade de Lisboa, Portugal)
- Simone Spolaor (University of Milano-Bicocca, Italy)
- Leonardo Trujillo (Instituto Tecnológico de Tijuana, Mexico)
- Leonardo Vanneschi (Universidade Nova de Lisboa, Portugal)
- Submission deadline:
15 January 202030 January 2020
- Notification: 15 March 2020
- Final paper submission: 15 April 2020
Special session papers should be uploaded online through the paper submission website of IEEE CEC 2020. Please select the corresponding special session name (“Special Session on candidate solutions representation and fitness landscape manipulation”) as the “main research topic” in submission. Papers in this session must both be appropriate to the session and meet the criteria for papers for WCCI 2020.
Daniel A. Ashlock, dashlock(at)uoguelph.ca
Professor, Department of Mathematics and Statistics, University of Guelph, ON, Canada
Marco S. Nobile, m.s.nobile(at)tue.nl
Assistant Professor, Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, The Netherlands
Joseph A. Brown, jbo3hf(at)gmail.com
Assistant Professor and Head of Artificial Intelligence in Game Development Lab at Innopolis University, Tartarstan, Russian Federation.
Luca Manzoni, lmanzoni(at)units.it
Assistant Professor, Department of Mathematics and Geosciences, University of Trieste, Italy