In global optimization techniques, the data structures used to represent the candidate solutions can sometimes be directly decoded and interpreted; consequently, the best-fitting individual immediately provides an explicit and human-readable description of the optimal solution. The researchers that mainly focused on the creation of novel strategies, often aim to propose solutions able to balance the exploration and exploitation capabilities of the optimization algorithms.
Although this well-established research line is very prolific—paving the way for the design of efficient algorithms, even for large-scale problems—there is another promising direction that should be investigated, consisting of the modification of the search space, that is, space transformations able to dilate, shrink, stretch, collapse, smooth, 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 the candidate solutions. In such a way, the genetic operators can explore the feasible search space in a “smarter” way, reducing the overall computational effort. Examples 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 to gather the researchers investigating new directions and ideas in the field of candidate solution representations and its dual notion of fitness landscape manipulation to tackle complex tasks, especially problems related to Bioinformatics and Computational Biology.
This Special Session welcomes any paper considering all kinds of non-conventional candidate solution representations, 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
- Highly interpretable representations
- Andrea Tangherloni (University of Bergamo, Italy – Bocconi University, Italy)
- Paolo Cazzaniga (University of Bergamo, Italy)
- Daniele M. Papetti (University of Milano-Bicocca, Italy)
- Riccardo Dondi (University of Bergamo, Italy)
- Daniela Besozzi (University of Milano-Bicocca, Italy)
- Giancarlo Mauri (University of Milano-Bicocca, Italy)
- Leonardo Rundo (University of Salerno, Italy)
- Luca Manzoni (University of Trieste, Italy)
- Marco S. Nobile (Ca' Foscari University, Italy)
- Mauro Castelli (Universidade Nova de Lisboa, Portugal)
- Sara Silva (Universidade de Lisboa, Portugal)
- Caro Fuchs (Eindhoven University of Technology, The Netherlands)
- Simone Spolaor (Eindhoven University of Technology, The Netherlands)
- Uzay Kaymak (Jheronimus Academy of Data Science, The Netherlands)
- Luca Mariot (Radboud University, The Netherlands)
- Simone G. Riva (University of Oxford, UK)
- Pietro Liò (University of Cambridge, UK)
- Gonzalo Ruz (Universidad Adolfo Ibanez, Chile)
- Paper Submission: 27 January 2023
- Paper Reviews: 17 March 2023
- Paper Re-submissions: 7 April 2023
- Paper Final Notifications: 14 April 2023
- Camera-Ready Manuscripts: 29 April 2023
Special session papers should be uploaded online through the paper submission website of IEEE CEC 2023.