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Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for benchmarking. For example, evaluation costs may be too high, or resources are unavailable (e.g., software or equipment). As a solution, data from previous evaluations can be used to train surrogate models which are then used for benchmarking. The goal is to generate test functions on which the performance of an algorithm is similar to that on the real-world objective function. However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived. This is especially problematic when the training data becomes sparse. The resulting benchmarks may not reflect the landscape features of the ground-truth, are too easy, and may lead to biased conclusions.
To resolve this, we use simulation of Gaussian processes instead of estimation (or prediction). This retains the covariance properties estimated during model training. While previous research suggested a decomposition-based approach for a small-scale, discrete problem, we show that the spectral simulation method enables simulation for continuous optimization problems. In a set of experiments with an artificial ground-truth, we demonstrate that this yields more accurate benchmarks than simply predicting with the Gaussian process model.
Recovery after extreme events - Lessons learned and remaining challenges in Disaster Risk Reduction
(2017)
Disasters such as the Indian Ocean Tsunami 2004, but also other extreme events such as cyclones, earthquakes and tsunami substantially affect the lives of many thousands of people - they are events radically and abruptly changing local circumstances and needs. At the same time they can significantly reshape global paradigms of Disaster Risk Reduction (DRR). Such events also bring to light the challenges in coordinating assistance from the “global community” with all the intended and un-intended effects. Two of the most pressing questions therefore are whether the different actors have learned from the disaster and whether processes of DRR and livelihood improvements have been implemented successfully.
This volume gathers selected papers addressing the following key questions:
- Lessons learned: Which lessons have been learned in a way that a difference can be seen today for the livelihoods and resilience of local people in the regions affected?
- Lessons to be Learned: Despite the body of knowledge created and reflected in a good number of lessons learned studies – what is still unsolved or needs to be emphasized?
- Monitoring and evaluation: Which DRR measures have been perpetuated and how can they be monitored and evaluated scientifically?
- Resilience effects and (unintended) side-effects: Which coping, recovery and adaptation measures are
supported by the resilience paradigm and which other areas are side-lined, neglected or even contrary to the intended effects?
- Dynamics in risk: In which cases has resilience building taken place? In which cases have ulnerabilities
been shifted internally or new vulnerabilities been created?
- Relocation/resettlement: How did the relocation/resettlement process of displaced people take place and what are its long-term effects?
- Urban-rural divide: How have DRR measures in urban vs. rural areas differed and which linkages but also rifts in rehabilitation and reconstruction initiatives can be observed between the two?
- Early warning: What is the future of Early Warning and how can important top-down information chains benefit from or be balanced with bottom-up feedback of users and affected people?
It appears that extreme disaster events spark a plethora of actions in academia, civil society, media, policy, private sector and other organisations. Tragic, as such disasters are, they offer incentives for learning, locally and globally. Lately, disaster impacts have in many cases been detracted through the application of knowledge and experience gained from previous events. However, there are still a number of challenges with regards to learning from past disasters