Proper coloring of the vertices of a graph with minimum number of colors has always been of great interest of researchers in the field of soft computing. Genetic Algorithm (GA) and its application as the solution method to the Graph Coloring problem have been appreciated and worked upon by the scientists almost for the last two decades. Various genetic operators such as crossover and mutation h...

This article introduce the genetic algorithm to solve the routing problem with QoS requirements, solve the traditional routing algorithms in completely the limitation of the NP problem. QoS routing optimization model is given, and the detailed design of genetic algorithm encoding scheme, fitness function, selection operator, crossover operator and mutation operator. Finally, the simulation expe...

We study an infinite population model for the genetic algorithm, where the iteration of the algorithm corresponds to an iteration of a map G. The map G is a composition of a selection operator and a mixing operator, where the latter models effects of both mutation and crossover. We examine the hyperbolicity of fixed points of this model. We show that for a typical mixing operator all the fixed ...

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 many Genetic Algorithms applications the objective is to nd a (near-)optimal solution using a limited amount of computation. Given these requirements it is diicult to nd a good balance between exploration and exploitation. Usually such a balance is found by tuning the various parameters (like the selective pressure, population size, the mutation-and crossover rate) of the Genetic Algorithm. ...

Maintaining the genetic diversity in populations is an important issue when dealing with dynamic environments. In this paper we use a modified Genetic Algorithm (GA) to solve the 0/1 Dynamic Knapsack Problem (DKP). The proposed GA uses a biologically inspired genetic operator instead of the classical crossover operator. The proposed genetic operator is capable of maintaining the genetic variati...

This paper presents an improved multi-objective evolutionary algorithm (IMOEA) for the design of planar steel frames. By considering constraints as a new objective function, single objective optimization problems turned to multi objective optimization problems. To increase efficiency of IMOEA different Crossover and Mutation are employed. Also to avoid local optima dynamic interference of mutat...

In this paper, we propose a technique for the application of the crossover operator that generates multiple descendants from two parents and selects the two best offspring to replace the parents in the new population. In crossover operator for real-coded genetic algorithms. In particular, we investigate the influence of the number of generated descendants in this operator, the xperimentation th...

1. Experiment design for parameters settings in SPGA There are several parameters that may influence the performance of the algorithm. For example, the larger population size may find better solution quality but cost larger computational expense. When the number of sub-populations is larger, it may have better diversity. However, it may also be a trade-off that to reduce the number of generatio...

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. ...