The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients passes) significantly affect overhead in terms computation time, transmission load, load. However, current practice manually selecting imposes heavy burden on practitio...

This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called SelfAdaptive Local Search (SALS), is proposed for obtaining q...

The Support Vector Machine (SVM) is a powerful and widely used classification algorithm. Its performance is well known to be impacted by a tuning parameter which is frequently selected by cross-validation. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM in the large and small tuning parameter regimes. These insig...

This thesis presents a set of rigorous methodologies for tuning the performance of algorithms that solve optimisation problems. Many optimisation problems are difficult and time-consuming to solve exactly. An alternative is to use an approximate algorithm that solves the problem to an acceptable level of quality and provides such a solution in a reasonable time. Using optimisation algorithms ty...

Parameter tuning is a key problem for statistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, resulting in parameters vulnerable to search errors in decoding. The recent research of “search-aware tuning” (Liu and Huang, 2014) addresses this problem by considering the partial derivations in every decoding step so that the promising ones a...