Add to ORKGInternational audienceThis work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set
The difficulty of solving a multi-objective optimization problem is impacted by the number of object...
International audienceThis paper proposes a new multi-objective genetic algorithm, called GAME, to s...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...
International audienceThis work studies the behavior of three elitist multi- and many-objective evol...
International audienceAchieving a high-resolution approximation and hitting the Pareto optimal set w...
International audienceIn this work we study the effects of population size on selection and performa...
Preprint - unpublishedIn evolutionary multi-objective optimization, effectiveness refers to how an e...
Over the past few decades, a plethora of computational intelligence algorithms designed to solve mul...
Abstract- Multi-objective evolutionary algorithms are widely established and well developed for prob...
The set of available multi-objective optimization algorithms continues to grow. This fact can be pa...
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University...
Selection methods are a key component of all multi-objective and, consequently, many-objective optim...
This empirical inquiry explores the behaviour of a particular class of evolutionary algorithms as th...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Studying the search behavior of evolutionary many objective optimization is an important, but challe...
The difficulty of solving a multi-objective optimization problem is impacted by the number of object...
International audienceThis paper proposes a new multi-objective genetic algorithm, called GAME, to s...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...
International audienceThis work studies the behavior of three elitist multi- and many-objective evol...
International audienceAchieving a high-resolution approximation and hitting the Pareto optimal set w...
International audienceIn this work we study the effects of population size on selection and performa...
Preprint - unpublishedIn evolutionary multi-objective optimization, effectiveness refers to how an e...
Over the past few decades, a plethora of computational intelligence algorithms designed to solve mul...
Abstract- Multi-objective evolutionary algorithms are widely established and well developed for prob...
The set of available multi-objective optimization algorithms continues to grow. This fact can be pa...
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University...
Selection methods are a key component of all multi-objective and, consequently, many-objective optim...
This empirical inquiry explores the behaviour of a particular class of evolutionary algorithms as th...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Studying the search behavior of evolutionary many objective optimization is an important, but challe...
The difficulty of solving a multi-objective optimization problem is impacted by the number of object...
International audienceThis paper proposes a new multi-objective genetic algorithm, called GAME, to s...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...