Genetic algorithms components

GAlib (C+ + Library of Genetic Algorithm Components), Massachusetts Institute of Technology, http //lancet.mU.edu/ga/... 

Hybrid systems (ANN-GA and SVMR-GA) were compared by Nandi et al. for their ability to model and optimize the isopropylation of benzene on Hbeta catalyst.The input parameters used to model the reaction were temperature, pressure, benzene-to-isopropyl alcohol ratio, and weight hourly space velocity. The output parameters were the yield of isopropylbenzene and the selectivity S, where S = 100 x (weight of isopropylbenzene formed per unit time)/(weight of total aromatics formed per unit time). Based on 42 experiments, the genetic algorithm component was used to select the optimum set of input parameters that maximize both yield and selectivity. The GA-optimized solutions were then verified experimentally, showing that the two hybrid methods can be used to optimize industrial processes. 

Kohonen network Conceptual clustering Principal Component Analysis (PCA) Decision trees Partial Least Squares (PLS) Multiple Linear Regression (MLR) Counter-propagation networks Back-propagation networks Genetic algorithms (GA)... 

Xue, L. and Bajorath, J. (2000) Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J. Chem. Inf. Comput. Sci. 40, 801-809. 

A separate class of experimental evaluation methods uses biological mechanisms. An artificial neural net (ANN) copies the process in the brain, especially its layered structure and its network of synapses. On a very basic level such a network can learn rules, for example, the relations between activity and component ratio or process parameters. An evolutionary strategy has been proposed by Miro-datos et al. [97] (see also Chapter 10 for related work). They combined a genetic algorithm with a knowledge-based system and added descriptors such as the catalyst pore size, the atomic or crystal ionic radius and electronegativity. This strategy enabled a reduction of the number of materials necessary for a study. 

The search was conducted by a genetic algorithm, which designed the compositions of the new set of catalysts to be screened. Each catalytic material consisted of three components (one support + one acidity enhancer + promoters) having for each catalyst set 24 new materials (Tab. 5.2 shows the compositions of the most active catalysts of each generation). Each catalyst set was synthesized and tested... 

Fig. 6.1 Operations used in genetic algorithms (values in the examples are proportions of elements in the active component of the catalyst expressed in mol%).

Thus, multilinear models were introduced, and then a wide series of tools, such as nonlinear models, including artificial neural networks, fuzzy logic, Bayesian models, and expert systems. A number of reviews deal with the different techniques [4-6]. Mathematical techniques have also been used to keep into account the high number (up to several thousands) of chemical descriptors and fragments that can be used for modeling purposes, with the problem of increase in noise and lack of statistical robustness. Also in this case, linear and nonlinear methods have been used, such as principal component analysis (PCA) and genetic algorithms (GA) [6]. 

The mind is like a computer program shaped by a genetic algorithm. It works, and works well, but it is hard to say how, and even harder to find out what its components are, to the extent that they exist at all. This may help to explain why psychology has, so far, not been a cumulative science, and why our attempts to discover the structure of the mind, and the structure of intelligence, are so difficult. If this is correct, an evolutionary approach, even though it may not lead us to discrete mental modules, may provide the key to understanding the mind s functional structure, and the neural mechanisms that mediate those functions. 

Hemmateenejad B, Akhond M, Miri R, Shamsipur M. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyri-dines (nifedipine analogs). J Chem Inf Comput Sci 2003 43 1328-34. 

---