Heterogeneous distributed genetic algorithms based on the crossover operator

1997 ◽  
Author(s):  
F. Herrera
Keyword(s):  
Genetic Algorithms ◽  
Crossover Operator ◽  
Distributed Genetic Algorithms

Robust confidence intervals applied to crossover operator for real-coded genetic algorithms

2007 ◽  
Vol 12 (8) ◽  
pp. 809-833 ◽  
Author(s):  
Domingo Ortiz-Boyer ◽  
César Hervás-Martínez ◽  
Nicolás García-Pedrajas
Keyword(s):  
Genetic Algorithms ◽  
Confidence Intervals ◽  
Crossover Operator ◽  
Real Coded Genetic Algorithms

scholarly journals EVOLUTIONARY OPTIMIZATION OF RBF NETWORKS

2001 ◽  
Vol 11 (03) ◽  
pp. 287-294 ◽  
Author(s):  
E. LACERDA ◽  
A. DE CARVALHO ◽  
TERESA LUDERMIR
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Optimization Criterion ◽  
Benchmark Problem ◽  
Crossover Operator ◽  
Rbf Networks ◽  
New Strategy ◽  
Free Parameters

One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This article discusses how Radial Basis Function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. A new strategy to optimize RBF networks using genetic algorithms is proposed, which includes new representation, crossover operator and the use of a multiobjective optimization criterion. Experiments using a benchmark problem are performed and the results achieved using this model are compared to those achieved by other approaches.

A New Crossover Operator for Assembly Sequence Planning by Genetic Algorithms

1996 ◽  
Author(s):  
Milad Fares Sebaaly ◽  
Hideo Fujimoto
Keyword(s):  
Genetic Algorithms ◽  
Decision Process ◽  
Genetic Material ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Crossover Operator ◽  
Optimal Sequence ◽  
Sequence Planning ◽  
Complete Set ◽  
Maximum Transmission

Abstract Assembly Sequence Planning (ASP) is the generation of the best or optimal sequence to assemble a certain product, given its design files. Although many planners were introduced in research to solve this problem automatically, it is still solved manually in many advanced assembly firms. The reason behind this is that most introduced planners are very sensitive to large increases in product parts. In fact, most of these planners seek the exact solution, while performing a part basis decision process. As a result, they are trapped in tedious and exhaustive search procedures, which make them inefficient and sometimes obsolete. To overcome these difficulties, Sebaaly and Fujimoto (1996) introduced a new concept of ASP based on Genetic Algorithms application, where the search procedure is performed on a sequence population basis rather than a part basis, and a best sequence is generated without searching the complete set of potential candidates. This paper addresses the problem of improving the GA performance for assembly application, by introducing a new crossover operator. The genetic material can be divided and classified as ‘good’ or ‘bad’. The new crossover insures the maximum transmission of ‘good’ features from one generation to another. This results in a faster GA convergence. The performance of the new algorithm is compared with that of the ordinary matrix crossover for a modified industrial example, where it proved to be faster and more efficient.

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