There are many optimization heuristics which involves mutation operator. Reducing them to binary optimization allows to study properties of binary mutation operator. Modern heuristics yield from Lévy flights behavior, which is a bridge between local search and random shooting in binary space. The paper is oriented to statistical analysis of binary mutation with Lévy flight inside and Quantum Tu...

Mutation is an important operator in genetic algorithms (GAs), as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs (RGAs) handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early ‘90s, the mutation operator is still implemented variable-wise – in a manner that is independ...

Recently introduced, evolutionary ontologies represent a new concept as a combination of genetic algorithms and ontologies. We have defined a new framework comprising a set of parameters required for any evolutionary algorithm, i.e. ontological space, representation of individuals, the main genetic operators such as selection, crossover, and mutation. Although a secondary operator, mutation pro...

Mutation is an important operator in genetic algorithms (GAs), as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs (RGAs) handle real-valued variables directly without going to in a binary string representation of variables. Although RGAs were first suggested in early nineties, the mutation operator is still implemented variablewise and independently for ea...

Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation oper...

This paper proposes a new type of mutation operator, FEDS (Fitness, Elitism, Depth, and Size) mutation in genetic programming. The concept behind the new mutation operator is inspired from already introduced FEDS crossover operator to handle the problem of code bloating. FEDS mutation operates by using local elitism replacement in combination with depth limit and size of the trees to reduce blo...

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...

Particle swarm optimization (PSO) is an efficient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that mutation operators can help PSO prevent premature convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compa...

ABSTRACT The purpose of selective mutation strategies is to reduce the inherent redundancy of full mutation analysis and hence obtain most of its bene t for a fraction of the cost. Unfortunately, recent research has shown that there is no xed selective mutation strategy that is e ective across a broad range of programs. In other words, for any given mutation operator, the utility (i.e., usefuln...

Mutation analysis is a well-known method for measuring the quality of test suites. However, it is computationally intensive compared to other measures, which makes it hard to use in practice. Choosing a smaller subset of mutations to run is a simple approach that can alleviate this problem. Mutation operator selection has been heavily researched. Recently, researchers have found that sampling m...