EvoCOMPLEX 2018 - 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