Add to ORKGIn the present work we study the options for parallelization of evolutionary algorithms for multiobjective optimization (MOGA). We provide the overview of existing sequential and parallel MOGAs and we propose three other methods: FCMOGA - MOGA with fuzzy constraints, HIMOGA - heterogeneous island MOGA, and MOGASOLS - MOGA with single objective local search. We test these algorithms on a set of benchmark problems and compare them with existing MOGAs
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...
The use of Evolutionary Algorithms (EAs) in difficult problems, where the search space is unknown, u...
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...
In the present work we study the options for parallelization of evolutionary algorithms for multiobj...
This paper deals with the study of the cooperation between parallel processing and evolutionary comp...
International audienceThis paper describes a unified view of parallel evolutionary algorithms for mu...
Parallel Computing 30 (2004) 721–739 This paper deals with the study of the cooperation between para...
Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scient...
Tomo antraštė: 15th International conference on information and software technologies, IT 2009 : Kau...
A multiobjective optimization problem involves several conflicting objectives and has a set of Paret...
Many real-world problems involve two types of difficulties: 1) multiple, conflicting objectives and ...
In the few last years, among other tools a multiobjective evolutionary algorithm (MOBEA) for succe...
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extens...
Using multiple local evolutionary searches, instead of single and overall search, has been an effect...
Global optimization problems are relevant in various fields of research and industry, such as chemis...
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...
The use of Evolutionary Algorithms (EAs) in difficult problems, where the search space is unknown, u...
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...
In the present work we study the options for parallelization of evolutionary algorithms for multiobj...
This paper deals with the study of the cooperation between parallel processing and evolutionary comp...
International audienceThis paper describes a unified view of parallel evolutionary algorithms for mu...
Parallel Computing 30 (2004) 721–739 This paper deals with the study of the cooperation between para...
Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scient...
Tomo antraštė: 15th International conference on information and software technologies, IT 2009 : Kau...
A multiobjective optimization problem involves several conflicting objectives and has a set of Paret...
Many real-world problems involve two types of difficulties: 1) multiple, conflicting objectives and ...
In the few last years, among other tools a multiobjective evolutionary algorithm (MOBEA) for succe...
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extens...
Using multiple local evolutionary searches, instead of single and overall search, has been an effect...
Global optimization problems are relevant in various fields of research and industry, such as chemis...
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...
The use of Evolutionary Algorithms (EAs) in difficult problems, where the search space is unknown, u...
We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm ...