This paper proposes a data clustering algorithm that combines the steady-state genetic algorithm and the ensemble learning method, termed as genetic-guided clustering algorithm with ensemble learning operator (GCEL). GCEL adopts the steady-state genetic algorithm to perform the search task, but replaces its traditional recombination operator with an ensemble learning operator. Therefore, GCEL c...

To solve the problem of the noise existed in feature items of category template in filtering system, weight adjusting strategy based on average fitness of population is proposed combining genetic algorithm with feedback. The feature items’ contribution to individual fitness is studied to adjust feature items’ weight by the genetic difference in the average fitness of the individual. Experimenta...

Genetic algorithms (GAs) are a highly effective and efficient means of solving optimization problems. Gene encoding, fitness landscape and genetic operations are vital to successfully developing a GA. F. Cheong and R. Lai (see ibid., vol. 30, p. 31-46 (2000)) described a novel method, which employed an enhanced genetic algorithm with multiple populations, to optimize a fuzzy controller, and the...

Since simple genetic algorithm based 2-D maximum entropy image segmentation algorithm has the problem of premature, this paper proposes an improved genetic algorithm. Through using Fitness Extreme Distance (FED), the improved genetic algorithm proposed in this paper establishes fuzzy evaluation mechanism in the evolution procedure. Compared with the simple genetic algorithm, improved algorithm ...

introduction: raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. material and methods: in this research, 153 raman spectra obtained from normal and dried skin samples. baseline and electrical noise were eliminat...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...

This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The applications of GA cover wide domains such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, etc. Fitness function is the measure of GA, distributed randomly in the population. Typically, th...

Significant research has been carried out recently to find the optimal path in network routing. Among them, the evolutionary algorithm approach is an area where work is carried out extensively. We in this paper have used particle swarm optimization (PSO) and genetic algorithm (GA) for finding the optimal path and the concept of region based network is introduced along with the use of indirect e...

In the recent years we were witnesses of an intense research activity, trying to find some common ground for quantum computation and genetic programming [4][19]. The use of genetic algorithms provides outstanding means for automated synthesis of quantum circuits. In this paper we focus on the opportunity of designing so-called Quantum Genetic Algorithms or QGAs. As the qubit representation of t...