In this paper, we investigate the multiple attribute decision making (MADM) problem based on the arithmetic and geometric aggregation operators with hesitant fuzzy linguistic information. Then, motivated by the idea of traditional arithmetic operation, we have developed some aggregation operators for aggregating hesitant fuzzy linguistic information: hesitant fuzzy linguistic weighted average (...

Genetic algorithms GAs are multi dimensional and stochastic search methods involving complex interactions among their parameters For last two decades re searchers have been trying to understand the mechanics of GA parameter interac tions by using various techniques careful functional decomposition of parameter interactions empirical studies and Markov chain analysis Although the complex ities i...

This paper analyses the behavior of the crossover operator in a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Three different crossover operators that limit the lengths of programs are analysed: A truncating operator, a limiting operator that constrains the lengths of both offspring and a limiting operator that only constrains the length of one offspring. ...

This paper presents the results of an investigation, and optimization of scheduling system in Sohar University (as a case study) using the genetic algorithm (GA). GA techniques are useful for solving real-world scheduling problem such as timetable which is a complex work and usually done manually. This work focuses on scheduling courses timetable to allocate events (time, subject, and lecturer)...

Geometric crossover is a representation-independent generalization of traditional crossover for binary strings. It is defined in a simple geometric way by using the distance associated with the search space. Many interesting recombination operators for the most frequently used representations are geometric crossovers under some suitable distance. Showing that a given recombination operator is a...

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...

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...

In ‎the present ‎paper, ‎we ‎introduce the ‎two-wavelet ‎localization ‎operator ‎for ‎the square ‎integrable ‎representation ‎of a‎ ‎homogeneous space‎ with respect to a relatively invariant measure. ‎We show that it is a bounded linear operator. We investigate ‎some ‎properties ‎of the ‎two-wavelet ‎localization ‎operator ‎and ‎show ‎that ‎it ‎is a‎ ‎compact ‎operator ‎and is ‎contained ‎in‎ a...

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator with...