Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Evolutionary Algorithms (EAs) have recently been successfully applied to numerical optimization problems. A major obstacle in the application of EAs has been the relatively slow convergence rate. This becomes more pronounced when the functions to be optimized become complex and numerically intensive. In this paper five different methods of speeding up EA convergence are reviewed. These include ...

This paper proposes a blade sorting method based on the cloud adaptive genetic algorithm (CAGA), which is used to optimize unbalanced of asymmetric rotor aero-engine. Firstly, by analyzing unbalance arrangement caused deviation mass moment blade, and considering concentricity disk, an optimization model amount assembly was established. Secondly, selection operator, crossover mutation operator w...

We first show that a bounded linear operator $ T $ on a real Banach space $ E $ is quasicompact (Riesz, respectively) if and only if $T': E_{mathbb{C}}longrightarrow E_{mathbb{C}}$ is quasicompact  (Riesz, respectively), where the complex Banach space $E_{mathbb{C}}$ is a suitable complexification of $E$ and $T'$ is the complex linear operator on $E_{mathbb{C}}$ associated with $T$. Next, we pr...

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this persp...

The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionar...

We present an improved hybrid genetic algorithm to solve the two-dimensional Euclidean traveling salesman problem (TSP), in which the crossover operator is enhanced with a local search. The proposed algorithm is expected to obtain higher quality solutions within a reasonable computational time for TSP by perfectly integrating GA and the local search. The elitist choice strategy, the local searc...

In this paper, the maximal dissipative extensions of a symmetric singular 1D discrete Hamiltonian operator with maximal deficiency indices (2,2) (in limit-circle cases at ±∞) and acting in the Hilbert space ℓ_{Ω}²(Z;C²) (Z:={0,±1,±2,...}) are considered. We consider two classes dissipative operators with separated boundary conditions both at -∞ and ∞. For each of these cases we establish a self...

This paper presents an evolutionary algorithm that uses the combination of the selection operator “the best” and the proposed operators, crossover “crossover-k” and intelligent mutation “mutation-S.” The initial population (feasible individuals) is generated with the kmeans algorithm of clustering (Data-Mining Techniques). The proposed algorithm solves the Vehicle Routing Problem with Time Wind...

In this paper, the notion of closure operators of matroids  is generalized to fuzzy setting  which is called $M$-fuzzifying closure operators, and some properties of $M$-fuzzifying closure operators are discussed. The $M$-fuzzifying matroid induced by an $M$-fuzzifying closure operator can induce an $M$-fuzzifying closure operator. Finally, the characterizations of $M$-fuzzifying acyclic matroi...