Genetic algorithm search method

A. Jin, F. Leung, and D. F. Weaver, ]. Comput. Chem., 18, 1971 (1997). Development of a Genetic Algorithm Search Method (GAP1.0) for Exploring Peptide Conformational Space. 

The approach of Chaudhury el al,131 can in principle be combined with any of the search methods we have discussed in Section 2 (as long as the Hessian can be calculated relatively easy — this seems to be the critical point of the approach), but Chaudhury et a/.131 combined it with a genetic-algorithm search. Subsequently they demonstrated its validity for clusters of Lennard-Jones atoms. 

Judson R S, W P Jaeger, A M Treasurywala and M L Peterson 1993. Conformational Searching Methods for Small Molecules. 2. Genetic Algorithm Approach. Journal of Computational Chemistry 14 1407-1414. 

Method and a Genetic Algorithm for Conformational Searching. Journal of Computational... 

To overcome the limitations of the database search methods, conformational search methods were developed [95,96,109]. There are many such methods, exploiting different protein representations, objective function tenns, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method [97], molecular dynamics simulations [92,110,111], genetic algorithms [112], Monte Carlo and simulated annealing [113,114], multiple copy simultaneous search [115-117], self-consistent field optimization [118], and an enumeration based on the graph theory [119]. 

Various search strategies can be used to locate the optimum. Indirect search strategies do not use information on gradients, whereas direct search strategies require this information. These methods always seek to improve the objective function in each step in a search. On the other hand, stochastic search methods, such as simulated annealing and genetic algorithms, allow some deterioration... 

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. 

In this case, systematic methods for generation of feasible molecular and mixture candidates have been reported for the design of functional chemical products [see Achenie et al (2003)]. Methods based on database search, total enumeration of feasible candidates (rule-based techniques that avoid a combinatorial explosion), mathematical programming, genetic algorithm, and,... 

Other methods have used genetic algorithms to search the 6D space one of them was originated... 

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... 

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