EvoCOMPLEX 2016 - 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 2016 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
attractors
convergence, computational complexity and stopping conditions
other applications of complex systems to EAs.