This special session is organized in association with the IEEE Computational Intelligence Society’s Technical Committee on Bioinformatics and Bioengineering (BBTC), and the IEEE CIS Task Force on advanced representation in biological and medical search and optimization.
In global optimization techniques, the data structures used to represent a candidate solution can sometimes be directly decoded and interpreted; as a consequence, 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 for 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, 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 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 at gathering the researchers who are investigating new directions and ideas in the field of candidate solution representation and its dual notion fitness landscape manipulation to tackle complex Bioinformatics and Computational Biology tasks.
This Special Session welcomes any paper considering all kinds of non-conventional candidate solution 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
- Highly interpretable representations
- Paolo Cazzaniga (University of Bergamo, Italy)
- Luca Manzoni (University of Trieste, Italy)
- Daniele M. Papetti (University of Milano-Bicocca, Italy)
- Andrea Tangherloni (University of Bergamo, Italy)
- Daniela Besozzi (University of Milano-Bicocca, Italy)
- Mauro Castelli (Universidade Nova de Lisboa, Portugal)
- Marco Della Vedova (University of Bergamo, Italy)
- Riccardo Dondi (University of Bergamo, Italy)
- Caro Fuchs (Eindhoven University of Technology, The Netherlands)
- Angelo Gargantini (University of Bergamo, Italy)
- Sheridan Houghten (Brock University, ON, Canada)
- Uzay Kaymak (Jheronimus Academy of Data Science, The Netherlands)
- Luca Mariot (Radboud University, The Netherlands)
- Giancarlo Mauri (University of Milano-Bicocca, Italy)
- Eric Medvet (University of Trieste, Italy)
- Marco S. Nobile (Ca' Foscari University, Italy)
- Leonardo Rundo (University of Salerno, Italy)
- Simone Spolaor (University of Milano-Bicocca, Italy)
- Submission deadline: 15 April 2022
- Notification: 15 June 2022
- Final paper submission: 15 July 2021
Special session papers should be uploaded online through the paper submission website of IEEE CIBCB 2022.