In this paper, we introduce a nature inspired meta-heuristic for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. The approach proposed is a variant of particle swarm optimization which uses mutation operator. The mutation operator ca...

Evolutionary algorithms as general randomized search heuristics typically perform a random search that is biased only by the fitness of the search points encountered. In practical applications the use of biased variation operators suggested by problem-specific knowledge may speed-up the search considerably. Problems defined over bit strings of finite length often have the property that good sol...

In this paper we present the outcome of a sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair. This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the genetic algorithm. Following implementation and testing of our genetic algotihm with GeneRepair we found a sign...

Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation o...

— Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient appro...