Optimizing decision problems under uncertainty can be done using a vari-ety of solution methods. Soft computing and heuristic approaches tend to be powerful for solving such problems. In this overview article, we survey Evolutionary Optimization techniques to solve Stochastic Programming problems- both for the single-stage and multi-stage case
AbstractEvolutionary programming is a stochastic optimization procedure that can be applied to diffi...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are ...
Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult ...
Jin Y, Branke J. Evolutionary Optimization in Uncertain Environments—A Survey. IEEE Transactions on ...
Abstract Many real problems with uncertainties may often be formulated as Stochastic Programming Pro...
Many real-world optimization problems have to face a lot of difficulties: they are often characteriz...
This book develops efficient methods for the application of Evolutionary Algorithms on stochastic pr...
This paper presents an extension of the previously developed approach to solve multiobjective optimi...
We consider the problem of finding a solution robust to disturbances of its decision variables, and ...
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dyn...
Abstract. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization...
Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and class...
Evolutionary algorithms have been widely used for a range of stochastic optimization problems in ord...
Many real-world optimization problems occur in environments that change dynamically or involve stoch...
AbstractEvolutionary programming is a stochastic optimization procedure that can be applied to diffi...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are ...
Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult ...
Jin Y, Branke J. Evolutionary Optimization in Uncertain Environments—A Survey. IEEE Transactions on ...
Abstract Many real problems with uncertainties may often be formulated as Stochastic Programming Pro...
Many real-world optimization problems have to face a lot of difficulties: they are often characteriz...
This book develops efficient methods for the application of Evolutionary Algorithms on stochastic pr...
This paper presents an extension of the previously developed approach to solve multiobjective optimi...
We consider the problem of finding a solution robust to disturbances of its decision variables, and ...
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dyn...
Abstract. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization...
Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and class...
Evolutionary algorithms have been widely used for a range of stochastic optimization problems in ord...
Many real-world optimization problems occur in environments that change dynamically or involve stoch...
AbstractEvolutionary programming is a stochastic optimization procedure that can be applied to diffi...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are ...