The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classe...

Abstract In order to solve the problems such as poor diversity and convergence ability of offspring population NSGA-II Algorithm in vehicle production scheduling problem, an improved shop algorithm based on is proposed. The NSGA-ii mainly focuses crossover mutation traditional Algorithm, proposes a new self-adaptive Crossover operator. By comparing individual crowding degree with average popula...

This paper presents some experimental results and analyses of the gene invariant genetic algorithm(GIGA). Although a subclass of the class of genetic algorithms, this algorithm and its variations represent a unique approach with many interesting results. The primary distinguishing feature is that when a pair of oospring are created and chosen as worthy of membership in the population they repla...

In this paper an evolutionary technique for detecting hierarchical structure of a data set is considered [4]. A linear representation of the cluster structure within the data set is used. An evolutionary algorithm evolves a population of clustering hierarchies. Proposed algorithm uses mutation and crossover as search (variation) operators. Binary tournament selection is considered. A new crosso...

In Vector Quantization (VQ), minimization of Mean Square Error (MSE) between code book vectors and training vectors is a non-linear problem. Traditional LBG type of algorithms converge to a local minimum, which depends on the initial code book. While most of the efforts in VQ have been directed towards designing efficient search algorithms for code book, little has been done in evolving a proce...

Abstract The problem of finding roots equations has always been an important research in the fields scientific and engineering calculations. For standard differential evolution algorithm cannot balance convergence speed accuracy solution, improved is proposed. First, one-half rule introduced mutation process, that is, half individuals perform evolutionary mutation, other strategy reorganization...

The problem of teacher placement in a school is faced by Magelang Regency. success determined the minimum total distance between and school, with aim that performance maintained. In computer science this an NP-hard takes very long time to achieve optimal results when done conventional methods. Another approach solve use heuristic algorithms, one which using genetic algorithms. To further improv...

In this paper, we consider the role of the crossover operator in genetic algorithms. Specifically, we study optimisation problems that exhibit many local optima and consider how crossover affects the rate at which the population breaks the symmetry of the problem. As an example of such a problem, we consider the subset sum problem. In doing so, we demonstrate a previously unobserved phenomenon,...

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutatio...