A genetic algorithm (GA) is an optimization method based on natural selection. Genetic algorithms have been applied to many hard optimization problems including VLSI layout optimization, boolean satis ability, power system control, fault detection, control systems, and signal processing. GAs have been recognized as a robust general-purpose optimization technique. But application of GAs to incre...

This work introduces a tool for the optimization of CMOS integrated spiral inductors. The main objective of this tool is to offer designers a first approach for the determination of the inductor layout parameters. The core of the tool is a Genetic Algorithm (GA) optimization procedure where technology constraints on the inductor layout parameters are considered. Further constraints regarding in...

This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best a...

Unit Commitment (UC) is an important optimization task in the daily operation planning of the utilities. In mathematical terms, UC is a nonlinear optimization problem with a varied set of constraints. In recent years, Genetic Algorithm (GA), as a powerful tool to achieve global optima, has been successfully used for the solution of this complex optimization problem. Nevertheless, since the GA d...

To design a more economical structural form, it is necessary to optimize both the topology and shape of structures. To optimize topology, we propose a hybrid of Genetic Algorithm (GA) and Evolutionary Structural Optimization (ESO). This paper describes the considerations in applying the proposed method to topology structural optimization. Through numerical examples, the proposed method showed b...

Traveling salesman problem (TSP) is proven to be NP-complete in most cases. The genetic algorithm (GA) is improved with two local optimization strategies for it. The first local optimization strategy is the four vertices and three lines inequality, which is applied to the local Hamiltonian paths to generate the shorter Hamiltonian circuits (HC). After the HCs are adjusted with the inequality, t...

An improved method based on a genetic algorithm (GA) is developed to design a broadband electrical impedance matching network for piezoelectric ultrasound transducer. A key feature of the new method is that it can optimize both the topology of the matching network and perform optimization on the components. The main idea of this method is to find the optimal matching network in a set of candida...

In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the TsaiHill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Alg...

In this research, the problem of structural damage detection using noisy frequency response function information is addressed. A methodology for damage detection is proposed that uses an unconstrained optimization problem formulation. To solve the optimization problem genetic algorithms (GA) and a local hillclimbing procedure were used. The inherent unstructured nature of damage detection probl...

This paper proposes a new improved Genetic Algorithm (GA) by utilizing a Data Mining technique, and demonstrates how it is superior to traditional GA on a popular job shop scheduling problem. GA has long been widely applied to solve complex optimization problems in a good variety of areas. It has advantages of adaptive capability, efficient search, potential to avoid local optimum, etc. In rece...