Differential Evolution (DE) proved to be one of the most successful evolutionary algorithms for global optimization purposes in continuous problems. The core operator in DE is mutation which can provide the algorithm with both exploration and exploitation. In this article, a new notation for DE is proposed which has a formula that can be utilized for generating and extracting novel mutations an...

A new mutation operator, called Mijn, capable of operating on a set of adjacent bits in one single step, is introduced. Its features are examined and compared against those of the classical bit{{ip mutation. A simple Evolutionary Algorithm, M{EA, is described which is based only on selection and Mijn. This algorithm is used for the solution of an industrial problem, the Inverse Aerofoil Design ...

A new evolutionary programming algorithm (NEP) using the non-uniform mutation operator instead of Gaussian or Cauchy mutation operators is proposed. NEP has the merits of “long jumps” of the Cauchy mutation operator at the early stage of the algorithm and “fine-tunings” of the Gaussian mutation operator at the later stage. Comparisons with the recently proposed sequential and parallel evolution...

Genetic Algorithms are the biologically inspired optimization algorithms that mimic the process of natural evolution. The performance of evolutionary algorithm in finding the optimal solutions can be broadly categorized into two strategies: exploration and exploitation, but it has been clearly shown in the literature that one cannot claim that which one is better than others in all the problems...

The main idea of the directed mutation is to focus on mutating into the most beneficial direction by using a customizable asymmetrical distribution. In this way the optimization strategy can adopt the most promising mutation direction over the generations. It thus becomes nearly as flexible as with Schwefel’s correlated mutation [2] but causes only linear growth of the strategy parameters inste...

Normally in Genetic Algorithms, mutation is considered a background operator and the genes are considered to be the binary bits of the chromosome. In this paper we take a different viewpoint. We treat the variables of numerical functions as the genes, and consider the mutation of these genes. We also investigate the role of mutation as an independent reproduction operator. Our results show the ...

Normally in Genetic Algorithms, mutation is considered a background operator and the genes are considered to be the binary bits of the chromosome. In this paper we take a di erent viewpoint. We treat the variables of numerical functions as the genes, and consider the mutation of these genes. We also investigate the role of mutation as an independent reproduction operator. Our results show the v...

Logic-based testers design tests from logical expressions that appear in software artifacts such as source code, design models, and requirements specifications. This paper presents three improvements to logic-based test design. First, in the context of mutation testing, we present fault hierarchies for the six relational operators. Applying the ROR mutation operator causes each relational opera...

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...

In this paper, we develop a novel clonal algorithm for multiobjective optimization (NCMO) which is improved from three approaches, i.e., dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM operator). Among them, the GP-HM operator is controlled by the dynamic mutation probability. ...