Meta-Heuristics: An Overview I.H. Osman, J.P. Kelly. Genetic Algorithms: A Parallel Genetic Algorithm for the Set Partitioning Problem D. Levine. Evolutionary Computation and Heuristics Z. Michalewicz. Gene Pool Recombination in Genetic Algorithms H. Muhlenbein, H.-M. Voigt. Genetic and Local Search Algorithms as Robust and Simple Optimization Tools M. Yagiura, T. Ibaraki. Networks and Graphs: Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree G. Craig, et al. An Aggressive Search Procedure for the Bipartite Drawing Problem R. Marti. Guided Search for the Shortest Path on Transportation Networks Y.M. Sharaiha, R. Thaiss. Scheduling and Control: A Metaheuristic for the Timetabling Problem H. Abada, E. El-Darzi. Complex Sequencing Problems and Local Search Heuristics P. Brucker, H. Hurink. Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties M. Dell'Amico, et al. Heuristics for the Optimal Control of Thermal E.
See Full PDF See Full PDF2007 IEEE Symposium on Computational Intelligence in Scheduling
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— Nowadays computers are also used to solve incredibly complex problems. To solve these problems we have to develop some advanced algorithms. Exact algorithms of such problems might need unacceptably huge time & space to discover the solutions. For making the solution-finding algorithms acceptable approximation algorithms have been developed. These approximation algorithms use the heuristics and meta-heuristics functions to find out the solutions. Heuristic algorithms use the special designed functions to find out solution space intelligently. Meta-heuristics algorithms are the iterative generation process which guides a subordinate heuristic for exploring and exploiting the search space. Learning strategies in meta-heuristics helps to find efficient near-optimal solutions. Meta-heuristic algorithms make the complex problems solvable in acceptable time. This survey paper is trying to explain heuristic and Meta-heuristic techniques to solve the complex problems.
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International Journal of Production Economics
