In this paper, a novel approach is proposed for contrast enhancement of greyscale image using the fuzzy logic based techniques such as contrast intensification (INT) operator. In the proposed algorithm, a measure of contrast of an image is introduced which is maximized to obtain a suitable crossover point for an image. Using this crossover point, the enhanced image is obtained using the INT ope...

This work analyzes fitness landscapes for the image filter design problem approached using functional-level Cartesian Genetic Programming. Smoothness and ruggedness of fitness landscapes are investigated for five genetic operators. It is shown that the mutation operator and the single-point crossover operator generate the smoothest landscapes and thus they are useful for practical applications ...

Evaluating a global optimal point in many global optimization problems in large space is required to more calculations. In this paper, there is presented a new approach for the continuous functions optimization with rotational mutation and crossover operator. This proposed method (RMC) starts from the point which has best fitness value by elitism mechanism and after that rotational mutation and...

In recent years, a variety of semantic operators have been successfully developed to improve the performance of GP. This work presents a new semantic operator based on the semantic crossover based on the partial derivative error. The operator presented here uses the information of the second partial derivative to choose a crossover point in the second parent. The results show an improvement wit...

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in binary-coded GAs. How...

ABSTRACT In this paper we study and compare the search properties of different crossover operators in genetic programming (GP) using probabilistic models and experiments to assess the amount of genetic material exchanged between the parents to generate the offspring. These operators are: standard crossover, onepoint crossover and a new operator, uniform crossover. Our analysis suggests that sta...

The mutation and cross-over operators are, with selection, the foundation of genetic algorithms. We show in this paper, some possibilities offered by these operators. Having explained the specificity of the most known operators (1-point, p-point and uniform cross-over, classical and deterministic mutation) we introduce new crossover and mutation operators with a low cost in term of execution ti...

Many experiments have proved that crossover is an essential search operator in evolutionary algorithms, at least for certain functions. However, the rigorous analysis of such algorithms on crossover-friendly functions is still in its infancy. Here a recombinative hill-climber is analyzed on the crossover-friendly function H-IFF introduced by [10]. The dynamics of this algorithm are investigated...

Many genetic algorithms use binary string or tree representations. We have developed a novel crossover operator for a directed and undirected graph representation, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator...