Genetic algorithm computer simulations

Azzaro-Pantel, C. L. Bemal-Haro P. Baudet S. Demenech, et al. A Two-Stage Methodology for Short-term Batch Plant Scheduling Discrete-Event Simulation and Genetic Algorithm Comput Chem Eng 22 1461-1481 (1998). 

Laquerbe, C Laborde, J.C. Soares, S. Floquet, P. Pibouleau L. Domenech, S. Synthesis of RTD models via stochastic procedures simulated annealing and genetic algorithms. Comput. Chem. Eng. 2001, 25, 1169-1183. 

To arrive at a true optimal subset of variables (wavelengths) for a given data set, consideration of all possible combinations should in principle be used but it is computationally prohibitive. Since each variable can either appear, or not, in the equation and since this is true with every variable, there are 2"-possible equations (subsets) altogether. For spectral data containing 500 variables, this means 2 possibilities. For this type of problems, i.e. for search of an optimal solution out of the millions possible, the stochastic search heuristics, such as Genetic Algorithms or Simulated Annealing, are the most powerful tools [14,15]. 

Azzaro-Pantel, C., Bernal Haro, L., Baudet, P., Domenech, S., Pibouleau, L. A two-stage methodology for short-term batch plant scheduling Discrete-Event Simulation and Genetic Algorithm, Computers Chem. Eng, Vol. 22, n°. 10, 1461-1482(1998). 

There are methods that deliberately avoid the use of gradient and Hessian information. Such approaches typically require many more iterations but can nevertheless save overall on computation. Some popular ones are the Simplex Method, Genetic Algorithms, Simulated Annealing, Particle Swarm and Ant Colony Optimization, and variants thereof. 

Several methods have been developed to aid in the selection of descriptors to create a QSAR model. The use of brute computational force to test every possible combination of the above problem is wasteful. Methods such as the Stepwise Searches (45), Simulated Annealing (94), and Genetic Algorithms... 

The methods of simulated annealing (26), genetic algorithms (27), and taboo search (29) are three of the most popular stochastic optimization techniques, inspired by ideas from statistical mechanics, theory of evolutionary biology, and operations research, respectively. They are applicable to our current problem and have been used by researchers for computational library design. Because SA is employed in this chapter, a more-detailed description of the (generalized) SA is given below. 

The developed optimization is solved with genetic algorithm as the previous study based on deterministic optimization techniques showed that it is often trapped in local optima, due to highly non-linear nature of formulations in the model. The simulation model and genetic algorithm is interacted to produce high quality optimal solution(s), although computational time is relatively expensive. 

Very popular and robust techniques like genetic algorithm (GA) and simulated annealing (SA) are used to solve such problems. The multiobjective forms of these techniques, e.g., NSGA-II (Deb et al., 2002) and MOSA (Suppapitnarm et al., 2000), are quite commonly used these days. These algorithms often require large amounts of computational (CPU) time. Any adaptation to speed up the solution procedure is, thus. 

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