Genetic algorithm fitness function

Table 11.3 One pass (read left to right) through the step.s of a basic genetic algorithm scheme to maximize the fitness function f x) = using a population of six 6-bit chromosomes. The crossover notation aina2) means that chromosomes Ca, and Ca2 exchange bits beyond the bit. The underlined bits in the Mutation Operation column are the only ones that have undergone random mutation. See text for other details.

The surface defined by an arbitrary fitness function, across which the genetic algorithm (GA) searches for a maximum or minimum. The surface may be complex and contain numerous minima and maxima. 

A new approach for computerized Inherent Safety Index is also presented. The index is used for the synthesis of inherently safer processes by using the index as a fitness function in the optimization of the process structure by an algorithm that is based on the combination of an genetic algorithm and case-based reasoning. Two case studies on the synthesis of inherently safer processes are given in the end. 

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 ... 

Algorithm GASP uses a genetic algorithm that iteratively optimizes a population of chromosomes according to the fitness function described here below [16]. Each chromosome encodes angles of rotations about flexible bonds and mappings between features. 

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