AbstractExploration efficiency of GAs largely depends on parameter values. But, it is hard to manually adjust these values. To cope with this problem, several adaptive GAs which automatically adjust parameters have been proposed. However, most of the existing adaptive GAs can adapt only a few parameters at the same time. Although several adaptive GAs can adapt multiple parameters simultaneously...

This paper introduces the notion of using coevolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm for numerical optimization. The proposed approach produces solutions even better than those previously reported in the literature for other (GA-based and mathematical programming) techniques that have been particularly fine-tuned using a normally lengthy t...

In this paper we implement GAs that have one or more parameters that are adjusted during the run. In particular we use an existing self-adaptive mutation rate mechanism, propose a new mechanism for self-adaptive crossover rates, and redesign an existing variable population size model. We compare the simple GA with GAs featuring only one of the parameter adjusting mechanisms and with a GA that a...

The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification...

Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a gene...

Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-a...

Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the al...

Artificial Neural Networks (ANNs) are the nonlinear and adaptive information processing systems which are combined by numerous processing units, with the characteristics of self-adapting, self-organizing and realtime learning, and play an important in pattern recognition, machine learning and data mining. But we’ve encountered many problems, such as the selection of the structure and the parame...

A permanent physical fault in communication lines usually leads to a failure. The feasibility of evolution of a self organized communication is studied in this paper to defeat this problem. In this case a communication protocol may emerge between blocks and also can adapt itself to environmental changes like physical faults and defects. In spite of faults, blocks may continue to function since ...

background: gestational diabetes (ga) is a common complication, associated with adverse prenatal outcomes and perceived stress. the effects of education on perceived stress have been widely investigated, whereas the role of self-care in ga has been less noted. aim: to determine the effects of self-care education on perceived stress in women with ga under insulin treatment. methods: in this rand...