Genetic algorithm constraints

P Willett, J Bradshaw and D V S Green 1999. Selecting Combinatorial Libraries to Optimize rsity and Physical Properties. Journal of Chemical Information and Computer Science 39 169-177. 1 and A W R Payne 1995. A Genetic Algorithm for the Automated Generation of Molecules in Constraints. Journal of Computer-Aided Molecular Design 9 181-202. 

Y Cm, RS Chen, WEI Wong. Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins 31 247-257, 1998. 

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. 

Chan Hilton, A. B., and Culver, T. B. (2000). "Constraint-handling methods for genetic algorithms in optimal pump-and-treat design." J. Water Resour Ping, and Mgnt Div., ASCE, 126(3), 128-137. 

Glen, R.C. andPayne, A.W.R. A genetic algorithm forthe automated generation of molecules within constraints. J. Comput.-Aid. Mol.Des., 1995,9,181-202. 

Payne and Glen (105) studied several different aspects of molecular recognition with genetic algorithms. Conformations and orientations were determined which best-fit constraints such as inter- or intramolecular distances, electrostatic surface potentials, or volume overlaps with up to 30 degrees of Ifee-dom. 

Husbands, P. (1994). Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation, in T. C. Fogarty (ed.). Evolutionary Computing. AIS Workshop. Selected Papers, Springer, LNCS Vol. 865 (Leeds, UK), pp. 150-165. 

Miettinen, K., Makela, M. and Toivanen, J. (2003b). Numerical comparison of some penalty-based constraint handhng techniques in genetic algorithms. Journal of Global Optimization 27, pp. 427-446. 

The 78 equality constraints in the complete model were thus reduced to 6 nonlinear equations as the genetic algorithm, NSGA-II-aJG is not effective in handling multiple equality constraints. Its inadequateness was also observed even when the equations had been reduced to 6 equations. Hence, the Broyden s update and finite-difference Jacobian function (DNEQBF) of the IMSL Library was embedded in the objective evaluation to solve the nonlinear equations 10.1 to 10.6. 

Glen R C and A W R Payme 1995 A Genetic Algorithm for the Automated Generation of Molecules within Constraints. Journal of Computer-Aided Molecular Design 9.181-202. 

Genetic algorithms (GAs) work with a coding of a parameter set, which in the field of chemical kinetics may consist of a number of parameters, such as rate coefficients variables and constraints, such as concentrations and other quantities such as chemical species. 

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