In this paper we present an enhanced evolutionary algorithm (EA) to solve computationally expensive design optimization problems. In this algorithm we integrate a genetic algorithm (GA) with a local search method to expedite convergence of the GA. We first use a GA to generate a population of data by evaluating real functions, then we construct computationally cheap surrogate models based on th...

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA...

A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-m...

The phytohormone gibberellin (GA) plays a key role in promoting stem elongation in plants. Previous studies show that GA activates its signaling pathway by inducing rapid degradation of DELLA proteins, GA signaling repressors. Using an activation-tagging screen in a reduced-GA mutant ga1-6 background, we identified AtERF11 to be a novel positive regulator of both GA biosynthesis and GA signalin...

Annular elastolytic giant cell granuloma (AEGCG) is a rare granulomatous skin disease of unclear pathogenesis which belongs to the group of disorders in the skin and elastic fibers with similar clinical features of granuloma annulare (GA). This case report is intended to describe a rare hybrid pattern in histopathology demonstrating coexistence of AEGCG and GA. An endocrine disease, such as dia...

In the desgin-process of a facility layout, the focus has been mainly on the placement of the machine resources. The material-ow, and therefore the desgin of the aisles has not appropriately been taken into account. We developed a hybrid genetic algorithm (GA) to generate the aisles in a facility layout at minimal xed and variable cost. The application of the GA to a practical problem leads to ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

This paper describes a hybrid Genetic Algorithm (GA) that is used to improve the layout of a graph according to a number of aesthetic criteria. The GA incorporates spatial and topological information by operating directly with a graph based representation. Initial results show this to be a promising technique for positioning graph nodes on a surface and may form the basis of a more general appr...

During the last decade, problems regarding the Traffic Management Advisor(TMA) has become a concerning matter. A novel hybrid Genetic Algorithm(GA) for the goal of seeking best possible alignment has been presented in this paper. This simple and yet very thorough method benefits from low computational burden, higher convergence rate and lower overall delays. Comprehensive simulations and implem...

Analog circuits are of great importance in electronic system design. Analog circuit design consists of circuit topology design and component values design. These two aspects are both essential to computer aided analog circuit evolving. However, Traditional GA is not very efficient in evolving circuit component’s values. This paper proposed a hybrid algorithm HME-GA (GA with hypermutation and el...