International audienceThis paper investigates the automatic parallelization of a heuristic for an NP-complete problem, with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the Savant syndrome. Its concurrency model is based on map-reduce. The approach is evaluated with the well-known Min-Min heuristic. Simulation results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality
Optimization problems arising in multiple fields of study demand efficient algorithms that can explo...
AbstractThis paper considers an unrelated parallel machine scheduling problem with the objective of ...
Since the task scheduling problem belongs to the strong NP-hard combinatorial optimization problem, ...
International audienceThis paper investigates the automatic parallelization of a heuristic for an NP...
Optimization problems arising in multiple fields of study demand efficient algorithms that can explo...
Many machine learning algorithms iteratively process datapoints and transform global model parameter...
This paper is devoted to the total tardiness minimization scheduling problem, where the efficiency o...
We describe in this paper a new approach to parallelize branch-and-bound on a certain number of proc...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
This paper investigates parallel machine scheduling problems where the objectives are to minimize to...
This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Ad...
In this work, we will look at a class of very hard practical problems which can, currently, only be ...
Scheduling problems are essential for decision making in many academic disciplines, including operat...
Optimization problems have been immuned to any attempt of combination with machine learning until a ...
The focus of this senior thesis is applying different machine learning optimization algorithms to di...
Optimization problems arising in multiple fields of study demand efficient algorithms that can explo...
AbstractThis paper considers an unrelated parallel machine scheduling problem with the objective of ...
Since the task scheduling problem belongs to the strong NP-hard combinatorial optimization problem, ...
International audienceThis paper investigates the automatic parallelization of a heuristic for an NP...
Optimization problems arising in multiple fields of study demand efficient algorithms that can explo...
Many machine learning algorithms iteratively process datapoints and transform global model parameter...
This paper is devoted to the total tardiness minimization scheduling problem, where the efficiency o...
We describe in this paper a new approach to parallelize branch-and-bound on a certain number of proc...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
This paper investigates parallel machine scheduling problems where the objectives are to minimize to...
This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Ad...
In this work, we will look at a class of very hard practical problems which can, currently, only be ...
Scheduling problems are essential for decision making in many academic disciplines, including operat...
Optimization problems have been immuned to any attempt of combination with machine learning until a ...
The focus of this senior thesis is applying different machine learning optimization algorithms to di...
Optimization problems arising in multiple fields of study demand efficient algorithms that can explo...
AbstractThis paper considers an unrelated parallel machine scheduling problem with the objective of ...
Since the task scheduling problem belongs to the strong NP-hard combinatorial optimization problem, ...