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...

In this paper, chaos based a new arithmetic crossover operator on the genetic algorithm has been proposed. The most frequent issue for the optimization algorithms is stuck on problem's defined local minimum points and it needs excessive amount of time to escape from them; therefore, these algorithms may never find global minimum points. To avoid and escape from local minimums, a chaotic arithme...

Genetic Algorithm (GAs) is used to solve optimization problems. It is depended on the selection operator, crossover and mutation rates. In this paper Roulette Wheel Selection (RWS) operator with different crossover and mutation probabilities, is used to solve well known optimization problem Traveling Salesmen Problem (TSP). We have compared the results of RWS with another selection method Stoch...

In this paper we explore the influence of the dynamics of evolution on coding structures of sequences. We show that, in systems with crossover, high mutation rates cause the rnoq conserved subsequences to he preferentially used as recognition sites for newly evolving sequences. In other words: “multiple coding” evolves in these systems. Multiple coding often does not increase. the fitness of th...

In this paper we give a representation-independent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this definition, a geometric/topological framework for evolutionary algorithms is introduced that clarifies the connection between representation, genetic operators, neighbourhood structure and distance in the landscape. Traditional gen...

This paper proposes a variant of genetic algorithm – GADYM, Genetic Algorithm with Gender-Age structure, DYnamic parameter tuning and Mandatory self perfection scheme. The motivation of this algorithm is to increase the diversity throughout the search procedure and to ease the difficulties associated with the tuning of GA parameters and operators. To promote diversity, GADYM combines the concep...

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator with...

One-point (or n-point) crossover has the property that schemata exhibited by both parents are ‘respected’transferred to the offspring without disruption. In addition, new schemata may, potentially, be created by combination of the genes on which the parents differ. Some argue that the preservation of similarity is the important aspect of crossover, and that the combination of differences (key ...

One-point (or n-point) crossover has the property that schemata exhibited by both parents are ‘respected’transferred to the offspring without disruption. In addition, new schemata may, potentially, be created by combination of the genes on which the parents differ. Some argue that the preservation of similarity is the important aspect of crossover, and that the combination of differences (key ...

In this work we present an application of the Confidence Interval Based Crossover using L2 Norm (CIXL2) and BLX-α crossovers to the evolution of neural networks. CIXL2 is a new crossover operator, based on obtaining the statistical features of the best individuals of the population. These features are used as virtual parents for the crossover operator. Due to the permutation problem of neural n...