In this paper, we propose a new channel routing algorithm based on genetic approach. This involves designing a new encoding scheme and an evaluation function used by the genetic algorithm(GA) for channel routing problem. The algorithm has been implemented as both sequential and distributed CA. The speedup achieved is positive and encouraging.

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parameter. The function of the mutation rate is similar to the function of temperature parameter in the simulated annealing [Lundy’86, Otten’89, and Romeo’85]. The parallel genetic algorithm presented here is based on a Markov chain [Kemeny’60] model. It has been proved that fine-grained parallel genet...

In this paper we present our implementation of a Genetic Algorithm on the BOINC volunteer computing platform. Our main objective is to construct a computational framework that applies to the optimum design problem of prairies. This ecology problem is characterized by a large parameter set, noisy multiobjective functions, and the presence of multiple local optima that reflects biodiversity. Our ...

A workforce planning problem is formulated here as a mixed-integer program and then solved with Cplex. Our experiments show that Cplex is capable of solving to optimality of near optimality small “structured” problem instances but it fails to solve those considered “unstructured”. We describe the development of a parallel genetic algorithm capable of finding high-quality solutions to both struc...

This paper presents an adaptive algorithm that can adjust parameters of genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. It is shown to be the most promising method from two performance measureme...

Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively—simulated annealing (SA) and genetic algorithms (GA)—do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a sel...

The hindrance of process scheduling in parallel and distributed system is challenging job in the field of computer science as research. Scheduling in parallel computing system plays an important role in overall for the enhancement of the performance of system in distributing the jobs. Scheduling in such type of system has a NP complete or NP hard problem. Process scheduling in parallel and dist...

In this paper a Grammar Guided Genetic Programmingbased method for the learning of rule-based classification systems is proposed. The method learns disjunctive normal form rules generated by means of a context-free grammar. The individual constitutes a rule based decision list that represents the full classifier. To overcome the problem of computational time of this system, it parallelizes the ...

An important issue in data mining is scalability with respect to the size of the dataset being mined. In the paper we address this issue by presenting a parallel GA for rule discovery. This algorithm exploits both data parallelism, by distributing the data being mined across all available processors, and control parallelism, by distributing the population of individuals across all available pro...