Add to ORKGAn approach based on a (m+1)-ES and three simple tournament rules is proposed to solve global optimization problems. The proposed approach does not use a penalty function and does not require any extra parameters other than the original parameters of an evolution strategy. This approach is validated with respect to the state-of-the-art techniques in evolutionary constrained optimization using a well-known benchmark. The results obtained are very competitive with respect to the approaches against which our approach was compared
This book presents powerful techniques for solving global optimization problems on manifolds by mean...
AbstractThe performance of an optimization tool is largely determined by the efficiency of the searc...
Part 2: Evolutionary ComputationInternational audienceNature-inspired algorithms attract many resear...
In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adapt...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlin...
Abstract- Several evolutionary approaches have been applied to global optimization problems with sig...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
Abstract. Nonlinear optimization problems introduce the possibility of multiple local optima. The ta...
This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimiz...
Abstract—Differential evolution (DE) is an efficient and powerful population-based stochastic search...
This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solv...
Constrained nonlinear programming problems involving a nonlinear objective function with inequality ...
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness fun...
Most real-world search and optimization problems are faced with constraints, which must be satisfied...
This book presents powerful techniques for solving global optimization problems on manifolds by mean...
AbstractThe performance of an optimization tool is largely determined by the efficiency of the searc...
Part 2: Evolutionary ComputationInternational audienceNature-inspired algorithms attract many resear...
In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adapt...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlin...
Abstract- Several evolutionary approaches have been applied to global optimization problems with sig...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
Abstract. Nonlinear optimization problems introduce the possibility of multiple local optima. The ta...
This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimiz...
Abstract—Differential evolution (DE) is an efficient and powerful population-based stochastic search...
This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solv...
Constrained nonlinear programming problems involving a nonlinear objective function with inequality ...
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness fun...
Most real-world search and optimization problems are faced with constraints, which must be satisfied...
This book presents powerful techniques for solving global optimization problems on manifolds by mean...
AbstractThe performance of an optimization tool is largely determined by the efficiency of the searc...
Part 2: Evolutionary ComputationInternational audienceNature-inspired algorithms attract many resear...