The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probabil...

Davis presented the order crossover operator for solving the travelling salesman problem (TSP) using genetic algorithm and has described two basic variants of this order crossover operator. In order to improve the efficiency and validity of these variants, in this paper three new variations of the order crossover operator are presented. These are programmed in C++ and implemented on a set of be...

Genetic algorithm (GA) is a very useful method for the global search of large search space and has been applied to various problems. It has two kinds of important search mechanisms, crossover and mutation. Because the performance of GA depends on these operators, a large number of operators have been developed for improving the performance of GA. Especially many researchers have more interested...

The crossover operator has always been regarded as the primary search operator in genetic algorithm (GA) because it exploits the available information from the population about the search space. Moreover, it is one of the components to consider for improving the behavior of the GA. To improve performance of GA multi parent crossover operators have been used. Multi parent crossover operators inv...

BACKGROUND The purpose of this study was to compare the two perimetric modalities, SWAP (short wavelength automated perimetry) and SAP (standard automated perimetry), on the point of conversion to glaucoma. METHODS In this prospective, longitudinal, follow-up study, 282 patients with ocular hypertension were recruited consecutively and tested with both SAP and SWAP annually for 5 years or unt...

. (1.2) In the manipulations above we have used the linearity of the superoperator M in the first line. In the second line we have used Lemma 0.4 with γ = I ⊗ σ , noting that |X|= s. The third line follows from the concavity of the square root. We will now use a helpful identity for the trace of an operator squared: TrZ =Tr(Z ⊗ Z)F , where F is defined as follows. Definition 1.1. The swap opera...

Several multi-parent crossover operators had been proposed to increase performance of genetic algorithms. The operators allow several parents to simultaneously take part in creating offspring. The operators need to balance between the two conflicting goals, exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exp...

In Vector Quantization (VQ), minimization of Mean Square Error (MSE) between code book vectors and training vectors is a non-linear problem. Traditional LBG type of algorithms converge to a local minimum, which depends on the initial code book. While most of the efforts in VQ have been directed towards designing efficient search algorithms for code book, little has been done in evolving a proce...

In this paper we present a method of evolving the contrast characteristics of a satellite image by employing a real-coded genetic algorithm with subjective fitness. The contrast properties of an image are given by its contrast-stretching curve, and the task of the Genetic Algorithm is to evolve this curve in order to find the best shape according to some subjective fitness. The subjective fitne...

The original analysis of genetic algorithms presents combination to be the primary mechanism of crossover. Although good solutions can be found by combination, they are often not locally optimal. Thus, a popular technique is to locally optimize each crossover solution before adding it to the population. In these \hybrid" operators, crossover can be viewed as a means of restarting the local opti...