Key words-Genetic Algorithm; Adaptive Genetic Algorithm; crossover probability; mutation probability; image segmentation

In genetic algorithms, commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained in the evolution because of their random choices of crossover points. To overcome this defect, a new adaptive crossover operator based on the Rough Set theory is proposed in this paper. By using this specialized crossover operator, u...

In this paper, we discuss multivariately factorized probability distributions for permutation random variables and a greedy approach to estimating these probability distributions from data. We use the representation known as random keys for permutations. The major benefit of using random keys is that no infeasible solution can be generated if crossover is applied in an evolutionary algorithm (E...

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator andmutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more ab...

The problem of decoding a binary linear code used over a binary symmetric channel so as to minimize bit error probability, rather than codeword error probability, is studied. In general, there are substantial differences with the maximum likelihood case. The optimal decoding rule depends on the crossover probability of the channel, the bit error probability depends on the generator matrix used,...

Exploration of the search space occurs at the cost of destructing existing good solutions. This cost will grow as the search progresses. The parametric uniform crossover is a general form of the uniform crossover operator. Using this operator, it would be possible to control the swapping probability of each locus. An adaptive method proposed that control the value of the exchange probability of...

Fisher distance based Genetic Algorithm: GA clustering method which takes into account overlapped space among probability density functions of clusters in feature space is proposed. Through experiments with simulation data of 2D and 3D feature space generated by random number generator, it is found that clustering performance depends on overlapped space among probability density function of clu...

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...