Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …

Algorithm configuration (AC) is concerned with the automated search of the most suitable
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

Algorithm selection (AS) techniques-which involve choosing from a set of algorithms the one
expected to solve a given problem instance most efficiently-have substantially improved the …

Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

Automatic algorithm configurators are important practical tools for improving program
performance measures, such as solution time or prediction accuracy. Local search …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

Good parameter settings are crucial to achieve high performance in many areas of artificial
intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and …

By representing the constraints and objective function in factorized form, graphical models
can concisely define various NP-hard optimization problems. They are therefore extensively …

An algorithm's parameter setting often affects its ability to solve a given problem, eg,
population-size, mutation-rate or crossover-rate of an evolutionary algorithm. Furthermore …