Genetic algorithms initialization

Simulated Annealing-based solutions [19] are conceptually the same as Genetic Algorithm-based approaches. However, the SA-based techniques, in our experience, are more sensitive to the initial settings of the parameters. Nevertheless, once the correct ones are found, the method can achieve the efficiency of GA-based solutions. We must point out that SA-based solutions have never outperformed the GA-based ones in our studies. Much of what has been mentioned regarding the GA-based solutions is also relevant for the SA technique, particularly, with respect to the cost functions. 

The size of initial population used in the genetic algorithm was 5 sequences. The size of crossover population was 2 sequences and the mutated population 2 sequences per generation. Consequently the total number of new sequences per generation was 4. The population size after selection was kept in 5. 

The genetic optimization was started with an initial population size of five, which was generated randomly. The algorithm included crossover of two sequences, which were selected randomly. Also random mutations were done on two sequences. The number of mutations per sequence varied from four in the beginning to one in the end per sequence. The steps of the genetic algorithm are ... 

Table 1 The Initial, Random Genetic Algorithm Population (The Significance of the Angles Marked in Bold is Discussed in the Text.)...

The second dataset consists of 50 V-acetyl peptide amides (Table 2) these peptides have un-ionizable side chains and have previously been studied by Buchwald and Bodor (28). The three-dimensional structures of the di-peptides were built using the force field and partial charges of Kollman (29) as implemented in Sybyl 6.5.3. The initial random starting conformations were energy minimized in vacuo. For all calculations described herein, the dielectric of the medium was set to unity and the electrostatic cut-off distance was set to 16 A. For each molecule, the Sybyl Genetic Algorithm-based conformational search,... 

These results were obtained by coupling a genetic algorithm for descriptor and calculation parameter (PC, bins) selection to PCA-based partitioning. In these calculations, descriptors were chosen from a pool of approx 150 different ones, and both the number of PCs and bins were allowed to vary from 1 to 15. An initial population of 300 chromosomes was randomly generated with initial bit occupancy of approx 15%. Rates for mutation and crossover operations were set to 5% and 25%, respectively. After PCA-based partitioning, scores were calculated for the following fitness function ... 

The program is reported to carry out simple Hiickel molecular orbital calculations to determine the relative sensitivity of aromatic carbon atoms to oxidation and the relative stability of keto and enol tautomers. Klopman et al. (1999) have reported that for polycyclic aromatic hydrocarbons, adequate reactivity is an essential but not sufficient condition for enzyme catalyzed reaction. The accessibility of the reactive site (i.e., the absence of steric hindrance) was also found to be important. Genetic algorithms have been used to optimize the performance of the biotransformation dictionary by treating the initial priority scores set by expert assessment as adjustable parameters (Klopman et al., 1997). 

---