The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value calculat...

among various heuristics techniques, genetic algorithm (ga) is one of the most widely used techniques which has successfully been applied on a variety of complex combinatorial problems. the performance of ga largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. in this p...

The search methods that have been developed to investigate the optimum point of any optimization problem are suffering from some shortcomings especially that are complex and NP-hard problems. The progress that have been achieved in the filed of information technology leads to developing a new search methods called "intelligent search methods". This paper highlights hybrid genetic scatter search...

Recently, genetic algorithms (GAs) and their hybrids have achieved great success in solving difficult combinatorial optimization problems. In this paper, the issues related to the performance of the genetic search in the context of the grey pattern problem (GPP) are discussed. The main attention is paid to the investigation of the solution recombination, i.e., crossover operators which play an ...

The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial,...

The optimization technique is used for the identification of some best values from the various populations. The Evolutionary algorithm is used as a basic concept of the Evolutionary Programming Strategy. To solve many of the numeric and combinatorial problems the evolutionary programming is applied. The optimization problem is obtained using the crossover and mutation. The mutation operation is...

This paper proposes a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving 0–1 quadratic knapsack problems. This is a combinatorial optimization problem, which arises in many fields of optimization. In S-bAFSA, trial points are created by using crossover and mutation. In order to make the points feasible, a random heuristic drop item procedure is used. The heur...

Transformation is a biologically inspired genetic operator that, when incorporated in the standard Genetic Algorithm can promote diversity in the population. Previous work using this genetic operator in the domain of function optimization and combinatorial optimization showed that the premature convergence of the population is avoided. Furthermore, the solutions obtained were, in general, super...