peer reviewedAchieving a balance between the exploration and exploitation capabilities of genetic algorithms is a key factor for their success in solving complicated search problems. Incorporating a local search method within a genetic algorithm can enhance the exploitation of local knowledge but it risks decelerating the schema building process. This paper defines some features of a local search method that might improve the balance between exploration and exploitation of genetic algorithms. Based on these features a probabilistic local search method is proposed. The proposed search method has been tested as a secondary method within a staged hybrid genetic algorithm and as a standalone method. The experiments conducted showed that the pro...
Recently, a hybrid methodology for combining genetic algorithms and local search algorithms has rece...
The aim of this paper is to clearly demonstrate the importance of finding a good balance between gen...
We report a series of experiments within a multiobjective genetic programming (GP) framework using s...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
Abstract — Achieving a balance between the exploration and exploitation capabilities of genetic algo...
The theory and practice of genetic algorithms is largely based on the Schema Theorem. It was formula...
Genetic algorithms (GAs), a class of evolutionary algorithms, emerging to be a promising procedure f...
In order to further improve the performance of current genetic algorithms aiming at discovering comm...
We report a series of experiments that use semantic-based local search within a multiobjective genet...
We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Pro...
The paper focuses on the efficiency of local search in a Hybrid evolutionary algorithm (HEA), with a...
Hybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide ...
Local search methods can harmoniously work with global search methods such as Evolutionary Algorithm...
One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the t...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
Recently, a hybrid methodology for combining genetic algorithms and local search algorithms has rece...
The aim of this paper is to clearly demonstrate the importance of finding a good balance between gen...
We report a series of experiments within a multiobjective genetic programming (GP) framework using s...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
Abstract — Achieving a balance between the exploration and exploitation capabilities of genetic algo...
The theory and practice of genetic algorithms is largely based on the Schema Theorem. It was formula...
Genetic algorithms (GAs), a class of evolutionary algorithms, emerging to be a promising procedure f...
In order to further improve the performance of current genetic algorithms aiming at discovering comm...
We report a series of experiments that use semantic-based local search within a multiobjective genet...
We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Pro...
The paper focuses on the efficiency of local search in a Hybrid evolutionary algorithm (HEA), with a...
Hybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide ...
Local search methods can harmoniously work with global search methods such as Evolutionary Algorithm...
One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the t...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
Recently, a hybrid methodology for combining genetic algorithms and local search algorithms has rece...
The aim of this paper is to clearly demonstrate the importance of finding a good balance between gen...
We report a series of experiments within a multiobjective genetic programming (GP) framework using s...