Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> International Journal of Computer Applications (0975 -8887) Volume 58-No.19, November 2012 <s> Abstract In this paper we present a genetic algorithm-based heuristic for non-unicost set covering problems. We propose several modifications to the basic genetic procedures including a new fitness-based crossover operator (fusion), a variable mutation rate and a heuristic feasibility operator tailored specifically for the set covering problem. The performance of our algorithm was evaluated on a large set of randomly generated problems. Computational results showed that the genetic algorithm-based heuristic is capable of producing high-quality solutions. <s> BIB001 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> International Journal of Computer Applications (0975 -8887) Volume 58-No.19, November 2012 <s> In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics. <s> BIB002 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> International Journal of Computer Applications (0975 -8887) Volume 58-No.19, November 2012 <s> In this paper, an improved guided genetic algorithm is proposed for the job-shop scheduling problem. The proposed method is improved by a genetic algorithm using multipliers which can be adjusted during the search process. Simulation results based on some benchmark problems demonstrate that the proposed method can find better solutions than the genetic algorithm and the original guided genetic algorithm. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 15–15, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10263 <s> BIB003 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> International Journal of Computer Applications (0975 -8887) Volume 58-No.19, November 2012 <s> In this study two combined mutation operators have been developed to increase the performance of Genetic Algorithm that helps to find the minimum cost in the known Travelling Salesman problem (TSP).In order to do this the combined mutation operators which are named as Inverted Exchange and Inverted Displacement are compared with four existing mutation operators. These are programmed in C++ and algorithms, ten bench mark test problems and five variations of order crossover are used. Based implemented on a set of benchmark test problems taken from the TSPLIB. The crossover operator used here is the order crossover using two of its existing and three of its new variations. In general six mutations on the numerical and graphical analysis it indicates that the Inverted Displacement has a definite supremacy over the existing four mutations except for few instances considered here. The Inversion mutation is second best performer. All optimal values found in the six mutations are obtained when the new variations of order crossover are used. <s> BIB004
Genetic algorithm uses evolutionary techniques to solve a solution of different problems. The basic step of a genetic algorithm is simple. At each iteration, a population of solution is evolved from one generation to the next generation. Next the better solutions are recombined with each other to form new solutions. Finally the new solutions generated will replace the poorest of the original solution and the process is repeated. Tate et al. (1995) proposed a genetic algorithm to solve quadratic assignment problem. Experiment illustrates that genetic algorithms find a better solution than those of the best previously known heuristics  . BIB001 proposed genetic algorithm to solve the set partitioning problem. In this paper basic genetic algorithm components such as fitness definition, parent selection and population replacement are modified. Experiment shows that genetic algorithm based heuristic is producing high quality solutions BIB001 .  proposed a genetic algorithm based heuristic for solving generalized assignment problem. In this approach, GA heuristic includes a problem-specific coding of a solution structure, a fitness-unfitness pair evaluation function and a local improvement procedure. Experiment shows that the genetic algorithm heuristic is able to find high quality solutions  . BIB002 proposed genetic algorithm for solving multidimensional knapsack problem. In this approach, a heuristic based upon genetic algorithm is introduced. The proposed heuristic is capable of obtaining high quality solutions to the problem BIB002 .  proposed genetic algorithm to solve the quadratic assignment problem. In this approach genetic algorithm includes many greedy principles in its design for solving the problem  . Olli Braijsy (2001) proposed genetic algorithm to solve vehicle routing problem with time window. In this paper, three evolutionary algorithms were described and compared with the other metaheuristic algorithm. Experiment shows genetic algorithm is not competitive with the best published result though the differences are not overwhelming  . Chiung Moon et al. (2002) proposed a genetic algorithm to solve traveling salesman problem with precedence constraints. In this paper, the key concept is the topological sort. Also a new crossover operation is introduced for the proposed genetic algorithm .Experiment shows that the proposed genetic algorithm produces an optimal solution and shows better performance compared to the traditional algorithm  . Omar et al. (2009) proposed an improved genetic algorithm to solve traveling salesman problem. In this approach the new crossover operation, population reformulates operation, multi mutation operation, partial local optimal mutation operation and rearrangement operation are used to solve the problem  . BIB003 proposed an improved genetic algorithm for solving scheduling problem. In this, the proposed approach is improved by a genetic algorithm using multipliers can be altered during the search process BIB003 . Kusum and Hadush (2011) proposed a genetic algorithm for solving traveling salesman problem. In this, two mutation operations named inverted exchange and inverted displacement are combined to increase the performance of genetic algorithm BIB004 .  proposed genetic algorithm to solve flow shop scheduling problem. In this, a genetic algorithm based heuristic is described to make span minimization on flow shop scheduling  . 