Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
A high-performance exact method for the resource-constrained project scheduling problem
Computers and Operations Research
Variable Neighborhood Decomposition Search
Journal of Heuristics
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Constraint Processing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
INFORMS Journal on Computing
Orthogonal neighborhood preserving discriminant analysis for face recognition
Pattern Recognition
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
A random key based genetic algorithm for the resource constrained project scheduling problem
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
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
An efficient hybrid algorithm for resource-constrained project scheduling
Information Sciences: an International Journal
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
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An effective hybrid evolutionary search method is presented which integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversifies the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective.