The primary target of the special session is to promote research on evolutionary computation in dynamic and uncertain environments. This is an emerging area in evolutionary computation, which covers the following different but closely related topics: Evolutionary computation (optimization) with noisy fitness evaluations, evolutionary computation (optimization) with approximate or imprecise fitness (quality) evaluations, evolutionary optimization where the design variables or the environmental parameters are subject to stochastic or deterministic changes, and evolutionary computation with a time-varying fitness function. This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field.

Many real-world optimization problems are subjected to dynamism and uncertainties that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation /measurement errors or approximation errors (in the case where surrogates are used in place of the computation ally expensive high fidelity fitness function). In addition, the design variables or environmental conditions can be perturbed or they change over time.

Although the last decade has witnessed increasing research efforts on handling dynamic and uncertain optimization problems using evolutionary algorithms and other metaheuristics, e.g., ant colony optimization, particle swarm optimization, artificial bee colony etc, dynamic optimizaition algorithms must keep pace with the times to solve more complex problems in practical applications. The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible, able to tolerate uncertainties, fast to allow reaction to changes and adaptive. Moreover, the objective of such tools is not only to locate the global optimum solution simply, but also to continuously track the optimum in dynamic environments, or to find a robust solution that operates properly in the presence of uncertainties.

This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field. Topics of interest include but are not limited to:

• Benchmark problems and performance measures
• Dynamic single - and multi-objective optimization
• Dynamic constrained optimization
• Adaptation, learning, and anticipation
• Models of uncertainty and their management
• Handling noisy fitness functions
• Using fitness approximations
• Searching for robust optimal solutions over time
• Algorithm comparison and benchmarking
• Hybrid approaches
• Theoretical analysis
• Real-world benchmarks and applications
• Online optimization in dynamic environments