Add to ORKGHybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide increased performances when compared to real coded GA or Local Search alone. However, the cost of Local Search can be rather high. In this paper we present a new hybrid algorithm which reduces the total cost of local search by avoiding the start of the method in basins of attraction where a local optimum has already been discovered. Additionally, the clustering information can be used to help the maintenance of diversity within the population.
Optimization problems can be found in many aspects of our lives. An optimization problem can be appr...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunc...
Hybridization of genetic algorithms with local search approaches can en-hance their performance in g...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Alt...
Genetic algorithms (GAs), a class of evolutionary algorithms, emerging to be a promising procedure f...
In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach for solvi...
This paper develops a framework for optimizing global-local hybrids of search or optimization proc...
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-sca...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
The aim of this paper is to clearly demonstrate the importance of finding a good balance between gen...
peer reviewedAchieving a balance between the exploration and exploitation capabilities of genetic al...
Evolutionary Algorithms are robust and powerful global optimization techniques for solving large sc...
Clustering is an unsupervised approach to extract hidden patterns from the datasets. There are certa...
One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the t...
Optimization problems can be found in many aspects of our lives. An optimization problem can be appr...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunc...
Hybridization of genetic algorithms with local search approaches can en-hance their performance in g...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Alt...
Genetic algorithms (GAs), a class of evolutionary algorithms, emerging to be a promising procedure f...
In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach for solvi...
This paper develops a framework for optimizing global-local hybrids of search or optimization proc...
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-sca...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
The aim of this paper is to clearly demonstrate the importance of finding a good balance between gen...
peer reviewedAchieving a balance between the exploration and exploitation capabilities of genetic al...
Evolutionary Algorithms are robust and powerful global optimization techniques for solving large sc...
Clustering is an unsupervised approach to extract hidden patterns from the datasets. There are certa...
One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the t...
Optimization problems can be found in many aspects of our lives. An optimization problem can be appr...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunc...
Hybridization of genetic algorithms with local search approaches can en-hance their performance in g...