The development of powerful search and optimization techniques is of great importance in science and engineering, particularly in today's world that requires researchers and practitioners to tackle a variety of challenging real-world problems as technology becomes an ever more important aspect of everyday life. There are two well-established and widely known fields that are addressing these issues: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics, such as evolutionary algorithms and genetic programming. Both of these fields have developed approaches with their unique strengths and weaknesses, allowing them to solve some challenging problems while sometimes failing in others.

The goal of the Numerical and Evolutionary Optimization (NEO) workshop series is to bring together people from all optimization fields to discuss, compare and merge their complimentary perspectives. NEO encourages the development of fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of each underlying paradigm, while also being applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems, particularly in emerging technologies that affect us.

Topics of interest include (but are not limited to):

A) Search and Optimization:

• Single- and multi-objective optimization
• Advances in evolutionary algorithms and genetic programming
• Hybrid and memetic algorithms
• Set oriented numerics
• Stochastic optimization
• Robust optimization

B) Real World Problems:

• Optimization, Machine Learning and Metaheuristics applied to:
• Energy production and consumption
• Health monitoring systems
• Computer vision and pattern recognition
• Energy optimization and prediction
• Modeling and control of real-world energy systems
• Smart cities