Modern heuristic techniques for combinatorial problems
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
Tabu Search
Integrated Production, Control Systems: Management, Analysis, and Design
Integrated Production, Control Systems: Management, Analysis, and Design
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scheduling and constraint propagation
Discrete Applied Mathematics
Solving Project Scheduling Problems by Minimum Cut Computations
Management Science
Ant Colony Optimization
Computers and Operations Research
Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Using an ant colony metaheuristic to optimize automatic word segmentation for ancient Greek
IEEE Transactions on Evolutionary Computation
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Resource management in the cloud using evolutionary computation
Proceedings of the 2012 SpringSim Poster & Work-In-Progress Track
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This paper presents a search method that combines elements from evolutionary and local search paradigms by the systematic use of crossover operations, generally used as structured exchange of genes between a series of solutions in genetic algorithms. Crossover operations here are particularly utilized as a systematic means to generate several possible solutions from two superior solutions. To test the effectiveness of the method, it has been applied to the resource-constrained project scheduling problem. The computational experiments show that the application of the method to this problem is promising.