Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answ...

Large high-clearance sprayers are widely used in the field of plant protection due to their high work efficiency. Influenced by characteristics a large ground clearance, fast driving speed and constantly changing sprung mass, how solve contradiction between vibration reduction performance sprayer friendliness farmland roads has become current research hotspot. In order improve sprayers, design,...

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first investigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operat...

This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and th...

This paper deals with some new operators of genetic algorithms for solving the traveling salesman problem (TSP). These include a new operator called, ”nearest fragment operator” based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. Superiority of these operators has been established on different benchmark data sets for symmetric TSP. Finally, th...

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...

We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

Evolutionary algorithms solve problems by simulating the evolution of a population candidate solutions. We focus on evolving permutations for ordering such as traveling salesperson problem (TSP), well assignment quadratic (QAP) and largest common subgraph (LCS). propose cycle mutation, new mutation operator whose inspiration is well-known crossover operator, concept permutation cycle. use fitne...

An important aspect of heterogeneous computing systems is the problem of efficiently mapping tasks to processors. There are various methods of obtaining acceptable solutions to this problem but the genetic algorithm is considered to be among the best heuristics for assigning independent tasks to processors. This paper focuses on how the genetic heuristic can be improved by determining the best ...