In this paper, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improve the convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency. we also use this new algorithm in data classification, select 5 benchmark datasets and the experiment results shown the new algorithm can get higher accuracy than k-nearest neighbor method
In this work a Genetic Algorithm coding and a required genetic operation library has been developed ...
Genetic algorithm is an algorithm that searches for the optimal solution by simulating the natural e...
Abstract Genetic Algorithm, an Artificial Intelligence approach is based on the theory of natural se...
The paper provides an improved evolutionary strategy (ES) of genetic algorithm (GA) on the basis of ...
Classification is the supervised learning technique of data mining which is used to extract hidden u...
Genetic algorithm is a classic intelligent bionic algorithm, which is evolved according to the genet...
In many heuristic optimization, it is easy to be trapped in local optimal. In contrast, genetic algo...
Traditional evolutionary algorithm trapped into the local minimum easily. Therefore, based on a simp...
Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but...
Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
K-means clustering is an important and popular technique in data mining. Unfortunately, for any give...
Abstract. a new genetic algorithm is proposed in the paper. Different from other genetic algorithms,...
Abstract—Genetic Algorithms uses the population selection technology, newer population is generated ...
The combinatorial optimization problem always is ubiquitous in various applications and has been pro...
In this work a Genetic Algorithm coding and a required genetic operation library has been developed ...
Genetic algorithm is an algorithm that searches for the optimal solution by simulating the natural e...
Abstract Genetic Algorithm, an Artificial Intelligence approach is based on the theory of natural se...
The paper provides an improved evolutionary strategy (ES) of genetic algorithm (GA) on the basis of ...
Classification is the supervised learning technique of data mining which is used to extract hidden u...
Genetic algorithm is a classic intelligent bionic algorithm, which is evolved according to the genet...
In many heuristic optimization, it is easy to be trapped in local optimal. In contrast, genetic algo...
Traditional evolutionary algorithm trapped into the local minimum easily. Therefore, based on a simp...
Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but...
Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
K-means clustering is an important and popular technique in data mining. Unfortunately, for any give...
Abstract. a new genetic algorithm is proposed in the paper. Different from other genetic algorithms,...
Abstract—Genetic Algorithms uses the population selection technology, newer population is generated ...
The combinatorial optimization problem always is ubiquitous in various applications and has been pro...
In this work a Genetic Algorithm coding and a required genetic operation library has been developed ...
Genetic algorithm is an algorithm that searches for the optimal solution by simulating the natural e...
Abstract Genetic Algorithm, an Artificial Intelligence approach is based on the theory of natural se...