Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesm...

This paper is aimed to find the optimum path of CNC turret typing system to reduce the changing tools times and optimize tool movement routes to make up for the deficiency of CNC Turret Typing machine production efficiency. An uncertainty polynomial model is raised based on the asymmetric traveling salesman problem. And genetic algorithm (GA) is used to solve the path optimization problem. The ...

in this paper we introduce two-wavelet constants for square integrable representations of homogeneous spaces. we establishthe orthogonality relations for square integrable representationsof homogeneous spaces which give rise to the existence of aunique self adjoint positive operator on the set of admissiblewavelets. finally, we show that this operator is a constant multiple of identity operator...

This paper presents a weight optimization method for nonlinear model predictive controller (NMPC) based on the genetic algorithm (GA) ship trajectory tracking. The coefficients Q and R of objective function in NMPC are obtained via real-time instead trial error method, which improves efficiency accuracy controller. In addition, targeted improvements made to internal crossover operator, mutation...

Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. t...

The aim of this paper is to show the influence of genetic operators such as crossover and mutation on the performance of a genetic algorithm (GA). The GA is applied to the multi-objective permutation flowshop problem. To achieve our goal an experimental study of a set of crossover and mutation operators is presented. A measure related to the dominance relations of different non-dominated sets, ...

In this paper, gene sets, instead of individual genes, are used in the genetic process to speed up convergence. A gene-set mutation operator is proposed, which can make several neighboring genes to simultaneously mutate. A gene-set crossover operator is also designed to choose the crossover points at the boundary of gene sets. The proposed gene-set mutation and crossover operators will cause a ...

Genetic algorithms (GAs) are multi-dimensional, blind heuristic search methods that involve complex interactions among parameters (such as population size, number of generations, GA operators and operator probabilities). The question whether the quality of results obtained by GAs depend upon the values given to these parameters, is a matter of research interest. This work studies the problem of...

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation o...

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...