Jérémie Dubois-Lacoste

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The program irace implements the iterated racing procedure, which is an extension of the Iterated F-race procedure (I/F-Race). Its main purpose is to automatically configure optimization algorithms by finding the most appropriate settings given a set of instances of an optimization problem. It builds upon the race package by Birattari and it is implemented(More)
Algorithms based on the two-phase local search (TPLS) framework are a powerful method to efficiently tackle multi-objective combinatorial optimization problems. TPLS algorithms solve a sequence of scalarizations, that is, weighted sum aggregations, of the multi-objective problem. Each successive scalarization uses a different weight from a predefined(More)
This paper presents new, carefully designed algorithms for five biobjective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) both, the weighted and nonweighted total tardiness of all jobs. The proposed algorithm combines two search(More)
Recent advances in automatic algorithm configuration have made it possible to configure very flexible algorithmic frameworks in order to fine-tune them for particular problems. This is often done by the use of automatic methods to set the values of algorithm parameters. A rather different approach uses grammatical evolution, where the possible algorithms(More)
The irace package implements the iterated racing procedure, which is an extension of Iterated F-race (I/F-Race). The main use of irace is the automatic configuration of optimization algorithms, that is, finding the most appropriate settings of an optimization algorithm given a set of instances of an optimization problem. It builds upon the race package by(More)
The automatic configuration of algorithms is a dynamic field of research. Its potential for producing highly performing algorithms may change the way we design algorithms. So far, automatic algorithm configuration tools have almost exclusively been applied to configure single-objective algorithms. In this paper, we investigate the usage of automatic(More)
This paper presents the steps followed in the design of hybrid stochastic local search algorithms for biobjective permutation flow shop scheduling problems. In particular, this paper tackles the three pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) the weighted total tardiness of all jobs.(More)
Two-Phase Local Search (TPLS) is a general algorithmic framework for multi-objective optimization. TPLS transforms the multi-objective problem into a sequence of single-objective ones by means of weighted sum aggregations. This paper studies different sequences of weights for defining the aggregated problems for the bi-objective case. In particular, we(More)