Population of chromosomes

Step 3 Generate a new size-K population of chromosomes from the old population... 

FIGURE 4.20 Scheme of a GA applied to variable selection. The first chromosome defines a variable subset with four variables selected from m = 10 variables. Fitness is a measure for the performance of a model built from the corresponding variable subset. The population of chromosomes is modified by genetically inspired actions with the aim to increase the... 

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. 

Create a (usually) random population of chromosomes that represent potential solutions. 

The optimum-seeking mecharusm of a GA is analogous to the biological evolutionary process. The GA operates on a population of chromosomes (also called individuals). Each chromosome represents a feasible solution to the problem investigated. Different representations have been developed to represent... 

The GA starts with an initial population of chromosomes. Either heuristic procedures or random creations can be used to generate feasible chromosomes that form the initial population. Anderson and Ferris (1994) have mentioned that the performance of the GA scheme is not as good from the pre-selected starting... 

When tackling a multi-objective problem by GAs, various approaches to fitness definition may be adopted Fonseca (1995). In what follows we will resort to the so called Pareto-based methods Goldberg (1989) once a population of chromosomes has been created, these are ranked according to the Pareto dominance criterion by looking at the /-dimensional space of the fitnessesfi U), i = Firstly,... 

Repeat steps (3) through (5) until a new population of chromosomes is created,... 

---