A Short Primer on Genetic Algorithms

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. 

Binary coding for a parameter is done as follows. Suppose we have a rate coefficient = 9.08 X 10 then if we write that rate coefficient as 10 , with —10 P 10, a binary coding with string length of 16 bits is given by 

For the solution of a given problem an optimization criterion must be chosen. With a given choice of parameters this criterion is calculated the comparison of that calculation with the goal set for the criterion gives a fitness value for that set of parameters. If the fitness is adequate but not sufficient, when both are selected by prior choice, for any individual, then retain that individual for the next generation. Reject individuals below that choice. 

Select individuals for the next generation with a probability proportional to the fitness value from a roulette wheel on which the slot size is proportional to the fitness value. Notice that genetic algorithms use probabilistic, not deterministic, transition rules. 

For the individuals retained from a prior generation we alter their chromosomes. One technique for doing that is mutation. Assign a probability of mutation for any bit in the chromosomes. By selection of random numbers between zero and one for each bit, determine if a mutation is to take place at that bit, from zero to one or vice versa. A mutation takes place at a bit only if the random number chosen for that bit exceeds the probability of the occurrence of a mutation. 

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