Evolutionary Algorithms and Complex Systems
Complex systems are ubiquitous in physics, economics, sociology, biology, computer science, and many other scientific areas. Typically, a complex system is composed of smaller aggregated components, whose interaction and interconnectedness are non-trivial (e.g., interactions can be high-dimensional and non-linear, and/or the connectivity can exhibit non-trivial topological features such as power-law degree distribution, and high clustering coefficient). This leads to emergent properties of the system, not anticipated by its isolated components. Furthermore, when the system behaviour is studied form a temporal perspective, self-organisation patterns typically arise.
Studying complex systems requires composite strategies that employ various different algorithms to solve a single difficult problem. Components of such strategies may solve consecutive phases leading to the main goal (for example, consider an oil deposit exploration strategy composed of a complex memetic search algorithm and of a direct FEM solver), may be used to approach particular sub-tasks from different perspectives (as, for example, in multi-scale approaches), or may solve the main problem in different ways that are aggregated to form the final solution (as, for example, in hyper-heuristics, island GAs or multi-physics approaches).
EvoCOMPLEX 2018 covers all aspects of the interaction of evolutionary algorithms -and metaheuristics in general- with complex systems. Topics of interest include, but are not limited to, the use of evolutionary algorithms for the analysis or design of complex systems, such as for example:
- complex networks, e.g., social networks, ecological networks, interaction networks, etc.
- chaotic systems- self-organizing systems, such as e.g., multiagent systems, social systems, etc.
- iterated function systems and cellular automata
- multi-scale, multi-physics and multi-goal systems
- other complex systems not included above
Relevant topics also include the use of complex systems and tools thereof to model, analyse or improve the performance of straightforward and complex evolutionary-based strategies evolutionary algorithms, such as for example:
- complex population structures
- synergy of component algorithms
- self-organized criticality and emergent behavior
- convergence, computational complexity and stopping conditions
- other applications of complex systems to EAs.
Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work.The reviewing process will be double-blind, please omit information about the authors in the submitted paper.
As in the previous edition of EvoCOMPLEX, a special issue of an impact journal is planned as a follow-up to the event. Authors of selected papers will be invited to submit extended versions of their work to this special issue. More details will be available after the conference.
Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.
Submit your manuscript in Springer LNCS format.
Please provide up to five keywords in your Abstract
Page limit: 16 pages.
EvoCOMPLEX Track Chairs
- Carlos Cotta
Universidad de Málaga, Spain
- Robert Schaefer
AGH University of Science and Technology,Poland