Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
Annals of Operations Research - Special issue on Tabu search
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Computers and Industrial Engineering
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Facts, Conjectures, and Improvements for Simulated Annealing
Facts, Conjectures, and Improvements for Simulated Annealing
Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Iterated robust tabu search for MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A local genetic algorithm for binary-coded problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
A novel stochastic optimization algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
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The flexible architecture of evolutionary algorithms allows specialised models to be obtained with the aim of resembling other algorithms, but performing more satisfactorily. In fact, several evolutionary proposals playing the role of local search methods have been proposed in the literature. In this paper, we make a step forward extending an innovative model recently proposed, which performs local search on external solutions, to match search process carried out by simulated annealing. We introduce acceptance criterion and cooling scheme concepts from simulated annealing, and modify some original components to better suit the new search process performed. An empirical study comparing the new model with classical simulated annealing algorithms shows that 1) the proposal is often able to reach good fitness values before than its competitors and 2) it suffers weaker convergence speed reductions that allow it to fruitfully continue the search process.