Multi-Tasking Evolutionary Algorithm (MTEA) google
Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel easy-to-implement multi-tasking evolutionary algorithm (MTEA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTEA outperformed all of them, and has lower computational cost than six of them. Particularly, unlike other multi-task algorithms, the performance of the MTEA is consistently good whether the tasks are similar or significantly different, making it ideal for real-world applications. …

Synthetic Difference In Difference (SDID) google
We present a new perspective on the Synthetic Control (SC) method as a weighted regression estimator with time fixed effects. This perspective suggests a generalization with two way (both unit and time) fixed effects, which can be interpreted as a weighted version of the standard Difference In Differences (DID) estimator. We refer to this new estimator as the Synthetic Difference In Differences (SDID) estimator. We validate our approach formally, in simulations, and in an application, finding that this new SDID estimator has attractive properties compared to the SC and DID estimators. In particular, we find that our approach has doubly robust properties: the SDID estimator is consistent under a wide variety of weighting schemes given a well-specified fixed effects model, and SDID is consistent with appropriately penalized SC weights when the basic fixed effects model is misspecified and instead the true data generating process involves a more general low-rank structure (e.g., a latent factor model). We also present results that justify standard inference based on weighted DID regression. …

Deep Optimisation google
Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way – not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper we extend its application to solving optimisation problems. Model Building Optimisation Algorithms (MBOAs), a branch of evolutionary algorithms, have been successful in combining machine learning methods and evolutionary search but, until now, they have not utilised DNNs. DO is the first algorithm to use a DNN to learn and exploit the problem structure to adapt the variation operator (changing the neighbourhood structure of the search process). We demonstrate the performance of DO using two theoretical optimisation problems within the MAXSAT class. The Hierarchical Transformation Optimisation Problem (HTOP) has controllable deep structure that provides a clear evaluation of how DO works and why using a layerwise technique is essential for learning and exploiting problem structure. The Parity Modular Constraint Problem (MCparity) is a simplistic example of a problem containing higher-order dependencies (greater than pairwise) which DO can solve and state of the art MBOAs cannot. Further, we show that DO can exploit deep structure in TSP instances. Together these results show that there exists problems that DO can find and exploit deep problem structure that other algorithms cannot. Making this connection between DNNs and optimisation allows for the utilisation of advanced tools applicable to DNNs that current MBOAs are unable to use. …