Genetic tabu search

Genetic algorithms Scatter search Simulated annealing Tabu search... 

Heuristic Search / 10.5.2 Tabu Search / 10.5.3 Simulated Annealing / 10.5.4 Genetic and Evolutionary Algorithms /... 

II with a new chapter (for the second edition) on global optimization methods, such as tabu search, simulated annealing, and genetic algorithms. Only deterministic optimization problems are treated throughout the book because lack of space precludes discussing stochastic variables, constraints, and coefficients. 

C. A. Baxter, C. W. Murray, D. E. Clark, D. R. Westhead, and M. D. Eldridge, Proteins Struct., Fund., Genet., 33, 367 (1998). Flexible Docking Using Tabu Search and an Empirical Estimate of Binding Affinity. 

Struct., Fund., Genet., 33, 367 (1998). Flexible Docking Using Tabu Search and an Empirical Estimate of Binding Affinity. 

Genetic algorithms Annealing techniques Dynamic programming Collocation methods Stochastic optimisation Agent-based computations Disjunctive programming Tabu search... 

Hou, T., Wang, J., Chen, L. Xu, X. (1999). Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search. Protein Eng 12(8), 639-48. 

Baxter CA, Murray CW, Clark DE, Westhead DR, Eldridge MD. Flexible docking using tabu search and an empirical estimate of binding affinity. Proteins Struct Funct Genet 1998 33 367-382. 

Cao T, Li T (2004) A combination of numeric genetic algorithm and tabu search can he applied to molecular docking. Comput Biol Chem 28(4) 303-312... 

Compare the optimization strategies of genetic algorithms, simulated annealing, and tabu search with those of local optimization methods. 

As an alternative to RSM, simulation responses can be used directly to explore the sample space of control variables. To do so, a lot of combinatorial optimization approaches were adapted for simulation optimization. In general, there are four main classes of methods that have shown a particular applicability in (multi-objective) simulation optimization Meta-heuristics, gradient-based procedures, random search, and sample path optimization. Of particular interest are meta-heuristics as they have shown a good performance for a wide range of combinatorial optimization approaches. Therefore, commercial simulation software primarily uses these techniques to incorporate simulation optimization routines. Among meta-heuristics, tabu search, scatter search, and genetic algorithms are most widely used. Table 4.13 provides an overview on aU aforementioned techniques. 

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