Abstract. In this paper, we propose a new constraint-handling technique for evolutionary algorithms which is based on multiobjective optimization concepts. The approach uses Pareto dominance as its selection criterion, and it incorporates a secondary population. The new technique is compared with respect to an approach representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. Results indicate that the proposed approach is able to match and even outperform the technique with respect to which it was compared at a lower computational cost.
In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi...
In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adapt...
Abstract—A common approach to constraint handling in evolutionary optimization is to apply a penalty...
Abstract—This paper presents a novel evolutionary algorithm for constrained optimization. During the...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrain...
This paper presents the main multiobjective optimization concepts that have been used in evolutionar...
Despite the extended applicability of evolutionary algorithms to a wide range of domains, the fact t...
This study presents a review of the current constraint handling strategies that are being employed i...
In optimization, multiple objectives and constraints cannot be handled independently of the underlyi...
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....
A criticism of Evolutionary Algorithms (EAs) might be the lack of efficient and robust generic might...
Abstract- A criticism of Evolutionary Algorithms (EAs) might be the lack of efficient and robust gen...
In this paper we present an evolutionary algorithm for constrained optimization. The algorithm is ba...
In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi...
In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adapt...
Abstract—A common approach to constraint handling in evolutionary optimization is to apply a penalty...
Abstract—This paper presents a novel evolutionary algorithm for constrained optimization. During the...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrain...
This paper presents the main multiobjective optimization concepts that have been used in evolutionar...
Despite the extended applicability of evolutionary algorithms to a wide range of domains, the fact t...
This study presents a review of the current constraint handling strategies that are being employed i...
In optimization, multiple objectives and constraints cannot be handled independently of the underlyi...
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....
A criticism of Evolutionary Algorithms (EAs) might be the lack of efficient and robust generic might...
Abstract- A criticism of Evolutionary Algorithms (EAs) might be the lack of efficient and robust gen...
In this paper we present an evolutionary algorithm for constrained optimization. The algorithm is ba...
In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi...
In this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adapt...
Abstract—A common approach to constraint handling in evolutionary optimization is to apply a penalty...