A method for solving satisfiability (SAT) instances is presented. It is based on two basic components: An Epistasis Reducer Algorithm (ERA) and a simple Genetic Algorithm (GA). ERA is implemented by a simulated annealing algorithm (SA), which preprocess the original SAT problem by rearranging the variables to satisfy the condition that the most related ones are in closer positions inside the ch...

In this paper an accurate method is presented for determining of the device sizes in a RF circuit based on genetic algorithm (GA). HSPICE RF simulation is used for evaluating of the fitness of the circuit specifications per every iteration of the GA. Also an example for a LNA is presented for evaluating of non-dominated sorting genetic algorithm (NSGA-II) as a method of multi objective genetic ...

A novel hybrid genetic algorithm (GA) for jointly optimizing source and channel codes is presented in this paper. The algorithm first uses GA for the coarse search of source and channel codes. An iterative search is then followed for the refinement of the coarse search. The hybrid GA enhances the robustness of the design of source and channel codes. The distributed GA scheme can also be used in...

For common misfiring fault of automobile engines, this paper presents an algorithm named GA-FNN which is the combination of the genetic algorithm (GA) and the fuzzy neural network (FNN). It calculates the memberships of inputs and initializes the weights and thresholds of the neural network by genetic algorithm firstly, and then trains the network and uses it for diagnosis. Simulation results d...

This paper discusses a parallel genetic algorithm (PGA) which focuses on the local operator for Traveling salesman problem (TSP). The local operator is a simple GA named as Local Genetic Algorithm (LGA). The LGA is combined to another GA named as Global Genetic Algorithm (GGA). It increases the computational time running a GA as a local operator in another one. To solve this problem, we build a...

This paper presents an optimal portfolio selection approach based on value at risk (VaR), conditional value at risk (CVaR), worst-case value at risk (WVaR) and partitioned value at risk (PVaR) measures as well as calculating these risk measures. Mathematical solution methods for solving these optimization problems are inadequate and very complex for a portfolio with high number of assets. For t...

The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...

Genetic Algorithms (GA) is well known for searching global maxima and minima[1, 2]. In general, number of search points required by GA for searching global extreme is much lower than the exhausted search. GA has been applied to Block Matching Algorithm (BMA) and demonstrates positively its capability in BMA. The mean square error (MSE) performance of GA based BMA is close to full search (FS). H...

Design of multiband antennas with low volume is of practical interest for the ever growing wireless communication industry. In this paper, the design of PIFA antenna with multiband resonance for WiMAX, WLAN, HIPERLAN/2 applications by using genetic algorithm (GA) is presented. Genetic algorithm (GA) method was used to design the shape of the patch and the shape of the ground plane simultaneousl...