The Genetic Algorithm (GA) is generally portrayed as a search procedure which can optimize pseudo-boolean functions based on a limited sample of the function's values. There have been many attempts to analyze the computational behavior of the GA. For the most part, these attempts have tacitly assumed that the algorithmic parameters of the GA (e.g. population size, choice of genetic operators, e...

The Genetic Algorithm (GA) is generally portrayed as a search procedure which can optimize pseudo-boolean functions based on a limited sample of the function's values. There have been many attempts to analyze the computational behavior of the GA. For the most part, these attempts have tacitly assumed that the algorithmic parameters of the GA (e.g. population size, choice of genetic operators, e...

In a recent paper [1], it was shown that a new Genetic Algorithm (GA) performed better than basic GA. In the present paper, we will focus an Improved Genetic Algorithm which uses the theory of cosets. The new technique was tested using some benchmark functions [2] which the basic GA performance was not good enough. The main purpose of this paper is to suggest how to use Linear Codes properties ...

This work studies the application of Genetic Algorithm (GA) to solve the timetable problem (TTP), a problem that is a highly constrained problem. However, the major disadvantage of GA is that the algorithm uses a tremendous amount of processing time. This paper proposes a method that can reduce the processing time by using Parallel Genetic Algorithm (PGA) on the Beowulf-style cluster computer. ...

A new image denoising algorithm is proposed. GA-ELM algorithm uses genetic algorithm (GA) to decide weights in the Extreme learning Machine algorithm. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-ELM to do image denoising researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utili...

background and objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. materials and met...

quad rotor is a renowned underactuated unmanned aerial vehicle (uav) with widespread military and civilian applications. despite its simple structure, the vehicle suffers from inherent instability. therefore, control designers always face formidable challenge in stabilization and control goal. in this paper fuzzy membership functions of the quad rotor’s fuzzy controllers are optimized using nat...

Clustering method for remote sensing satellite image classification based on Messy Genetic Algorithm: GA is proposed. Through simulation study and experiments with real remote sensing satellite images, the proposed method is validated in comparison to the conventional simple GA. It is also found that the proposed clustering method is useful for preprocessing of the classifications. Keywordsclus...

The aim of this work is to present some alternatives to improve the performance of an Evolutionary Algorithm applied to the problem known as the Oil Collecting Vehicle Routing Problem. Some proposals based on the insertion of Local Search and Data Mining modules in a Genetic Algorithm (GA) are presented. Four algorithms were developed: a Genetic Algorithm, a Genetic Algorithm with a Local Searc...

We propose a powerful Reinforced Hybrid Genetic Algorithm (RHGA) for the famous NP-hard Traveling Salesman Problem (TSP). RHGA combines reinforcement learning technique with well-known Edge Assembly Crossover genetic algorithm (EAX-GA) and Lin-Kernighan-Helsgaun (LKH) local search heuristic. With help of proposed hybrid mechanism, evolution EAX-GA LKH can boost each other's performance. And bas...