Genetic Algorithms (GA) has been widely used for logic optimization and synthesis with a view to optimize area, power or testability and for various trades-offs. Traditional logic optimizers such as ESPRESSO targets area minimization only, while present day device scaling demand for extremely low power consumption in VLSI circuits. This paper compares the performances of two types of genetic al...

Genetic algorithms (GAs) have proved to be a versatile and eeective approach for solving combinatorial optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. These often involve incorporating other methods such as simulated annealing or local optimization as anàdd-on' extr...

Security, integrity, non-repudiation, confidentiality, and authentication services are the most important factors in information security. Genetic algorithms (GAs) are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modeling a simplified version of genetic processes. The application of a genetic algorithm (GA) to the field of cryptology is rather...

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Ge...

In this paper, Genetic Algorithms (GA) has been applied to calculate the optimized parameters of Elliptical Microstrip antenna. The fitness function has been developed in GA and the optimized dimensions of the antenna have been calculated. The output obtained is the optimized semimajor axis length ‘a’ from which the other parameters i.e. semi-minor axis length and odd mode frequency are calcula...

Computing the bandwidth-delay-constrained least-cost multicast routing tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed method, the connectivity matrix of edges is used for genotype representation. Some novel heuristic algorithms are also proposed for mutation, crossover, and creation of...

The bandwidth-delay-constrained least-cost multicast routing is a challenging problem in high-speed multimedia networks. Computing such a constrained Steiner tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed method, the predecessors encoding is used for genotype representation. Some novel...

Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibili...

−We investigate the performance of a classical constrained optimisation (CCO) algorithm and a constrained optimisation Genetic Algorithm (GA) for solving the Bandwidth Allocation for Virtual Paths (BAVP) problem. We compare throughput, fairness and time complexity of GA-BAVP and CCO-BAVP for several node topologies. The results on maximising the throughput obtained with GA-BAVP and CCOBAVP are ...

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...