The purpose of this paper is to discuss the implementation and performance of two genetic operators specifically tuned to solve the Travelling Salesman Problem. The two operators discussed are the Greedy Knot-Cracker crossover operator and a modified version of the 3-opt mutation operator. Specifically the paper shall discuss the motivation for choosing these operators, their implementation, th...

This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from popula...

Recently, genetic algorithms (GAs) and their hybrids have achieved great success in solving difficult combinatorial optimization problems. In this paper, the issues related to the performance of the genetic search in the context of the grey pattern problem (GPP) are discussed. The main attention is paid to the investigation of the solution recombination, i.e., crossover operators which play an ...

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...

This article proposes a new approach for determining the optimal parameter (β) in Inverse Distance Weighted Method (IDW) spatial interpolation of hydrological data series. is based on genetic algorithm (GA) and finds unique β entire study region, while classical one determines different βs interpolated The proposed four scenarios crossover/mutation: single-point/uniform, single-point/swap, two-...

In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One point crossover presents very interesting properties from the theory po...

In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One-point crossover presents very interesting properties from the theory po...

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it ...

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it ...

Crossover operator is the predominant operator in most of Genetic Programming (GP) system. The empirical evidence shows that along with building blocks are constructed bigger and bigger as GP evolution proceeds, the crossover operator tends to disrupt those building blocks rather than preserve them. The traditional GP crossover primarily acts as macromutation. Looseness is used for representing...