AbstractAlgorithms for parameter optimization display subthreshold-seeking behavior when the majority of the points that the algorithm samples have an evaluation less than some target threshold. We first analyze a simple “subthreshold-seeker” algorithm. Further theoretical analysis details conditions that allow subthreshold-seeking behavior for local search algorithms using Binary and Gray code representations. The analysis also shows that subthreshold-seeking behavior can be increased by using higher bit precision. However, higher precision also can reduce exploration. A simple modification to a bit-climber is proposed that improves its subthreshold-seeking behavior. Experiments show that this modification results in both improved search e...
International audienceDespite the huge number of studies in the metaheuristic field, it remains diff...
It is well known that the performance of a stochastic local search procedure depends upon the setti...
This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been consi...
AbstractAlgorithms for parameter optimization display subthreshold-seeking behavior when the majorit...
This paper describes algorithms that learn to improve search performance on large-scale optimization...
Abstract. Tuning stochastic local search algorithms for tackling large instances is difficult due to...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
We present a computational performance analysis of local search algorithms for job shop schedul-ing....
AbstractEmpirical performance optimization of computer codes using autotuners has received significa...
It has recently been shown that local search is sur-prisingly good at nding satisfying assignments f...
Proving that a program is correct can be done by translating it into a first-order formula and tryin...
In this thesis, we show how an Extended Guided Local Search can be applied to a set of problems and ...
Local search is a widely used method to solve combinatorial optimization problems. As many relevant ...
International audienceThe paper is concerned with function optimisation in binary search spaces. It ...
Abstract. Machine learning can be utilized to build models that predict the runtime of search algori...
International audienceDespite the huge number of studies in the metaheuristic field, it remains diff...
It is well known that the performance of a stochastic local search procedure depends upon the setti...
This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been consi...
AbstractAlgorithms for parameter optimization display subthreshold-seeking behavior when the majorit...
This paper describes algorithms that learn to improve search performance on large-scale optimization...
Abstract. Tuning stochastic local search algorithms for tackling large instances is difficult due to...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
We present a computational performance analysis of local search algorithms for job shop schedul-ing....
AbstractEmpirical performance optimization of computer codes using autotuners has received significa...
It has recently been shown that local search is sur-prisingly good at nding satisfying assignments f...
Proving that a program is correct can be done by translating it into a first-order formula and tryin...
In this thesis, we show how an Extended Guided Local Search can be applied to a set of problems and ...
Local search is a widely used method to solve combinatorial optimization problems. As many relevant ...
International audienceThe paper is concerned with function optimisation in binary search spaces. It ...
Abstract. Machine learning can be utilized to build models that predict the runtime of search algori...
International audienceDespite the huge number of studies in the metaheuristic field, it remains diff...
It is well known that the performance of a stochastic local search procedure depends upon the setti...
This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been consi...