In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adaptation that uses three tournament rules based on feasibility) coupled with a diversity mechanism to solve constrained optimization problems. The proposed mechanism is based on multiobjective optimization concepts taken from an approach called the Niched-Pareto Genetic Algorithm (NPGA). The main advantages of the proposed approach is that it does not require the definition of any extra parameters, other than those required by an evolution strategy. The performance of the proposed approach is shown to be highly competitive with respect to other constraint-handling techniques representative of the state-of-the-art in the area when using a set of ...
A new constraint handling technique for multi-objective genetic algorithm is proposed in this paper....
In optimization, multiple objectives and constraints cannot be handled independently of the underlyi...
Often the Pareto front of a multi-objective optimization problem grows exponentially with the proble...
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlin...
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrain...
Abstract—This paper presents a novel evolutionary algorithm for constrained optimization. During the...
In this paper we present an evolutionary algorithm for constrained optimization. The algorithm is ba...
An approach based on a (m+1)-ES and three simple tournament rules is proposed to solve global optimi...
Abstract. In this paper, we propose a new constraint-handling technique for evolutionary algorithms ...
Constrained optimization is a challenging area of research in the science and engineering discipline...
In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve...
Most real-world search and optimization problems are faced with constraints, which must be satisfied...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
A new constraint handling technique for multi-objective genetic algorithm is proposed in this paper....
In optimization, multiple objectives and constraints cannot be handled independently of the underlyi...
Often the Pareto front of a multi-objective optimization problem grows exponentially with the proble...
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlin...
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrain...
Abstract—This paper presents a novel evolutionary algorithm for constrained optimization. During the...
In this paper we present an evolutionary algorithm for constrained optimization. The algorithm is ba...
An approach based on a (m+1)-ES and three simple tournament rules is proposed to solve global optimi...
Abstract. In this paper, we propose a new constraint-handling technique for evolutionary algorithms ...
Constrained optimization is a challenging area of research in the science and engineering discipline...
In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve...
Most real-world search and optimization problems are faced with constraints, which must be satisfied...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
A new constraint handling technique for multi-objective genetic algorithm is proposed in this paper....
In optimization, multiple objectives and constraints cannot be handled independently of the underlyi...
Often the Pareto front of a multi-objective optimization problem grows exponentially with the proble...