International audienceA method to generate various size tunable benchmarks for multi-objective AI planning with a known Pareto Front has been recently proposed in order to provide a wide range of Pareto Front shapes and different magnitudes of difficulty. The performance of the Pareto-based multi-objective evolutionary planner DaEYAHSP are evaluated on some large instances with singular Pareto Front shapes, and compared to those of the single-objective aggregation-based approach
After adequately demonstrating the ability to solve different two-objective optimization problems, m...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique objecti...
Abstract- Multi-objective evolutionary algorithms are widely established and well developed for prob...
International audienceA method to generate various size tunable benchmarks for multi-objective AI pl...
International audienceMost real-world Planning problems are multi-objective, trying to minimize both...
International audienceMulti-objective AI planning suffers from a lack of bench-marks with known Pare...
International audienceReal-world problems generally involve several antagonistic objectives, like qu...
International audienceAll standard AI planners to-date can only handle a single objective, and the o...
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In thi...
Over the past few decades, a plethora of computational intelligence algorithms designed to solve mul...
In this work, we propose a framework to accelerate the computational efficiency of evolutionary algo...
International audienceThis paper proposes a new multi-objective genetic algorithm, called GAME, to s...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem i...
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown to be very effi...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique...
After adequately demonstrating the ability to solve different two-objective optimization problems, m...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique objecti...
Abstract- Multi-objective evolutionary algorithms are widely established and well developed for prob...
International audienceA method to generate various size tunable benchmarks for multi-objective AI pl...
International audienceMost real-world Planning problems are multi-objective, trying to minimize both...
International audienceMulti-objective AI planning suffers from a lack of bench-marks with known Pare...
International audienceReal-world problems generally involve several antagonistic objectives, like qu...
International audienceAll standard AI planners to-date can only handle a single objective, and the o...
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In thi...
Over the past few decades, a plethora of computational intelligence algorithms designed to solve mul...
In this work, we propose a framework to accelerate the computational efficiency of evolutionary algo...
International audienceThis paper proposes a new multi-objective genetic algorithm, called GAME, to s...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem i...
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown to be very effi...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique...
After adequately demonstrating the ability to solve different two-objective optimization problems, m...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique objecti...
Abstract- Multi-objective evolutionary algorithms are widely established and well developed for prob...