Programming is a kind of art, that has been gained by the chosen…
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A genetic algorithms series.
In this post I present an implementation of the Classic Genetic Algorithm version, as an interactive widget, using binary representation of chromosome (we use 0/1 bits as a genes in chromosome, to encode variables). As you may know from previous post, this king of representation make operators easier to implement.
Chromosome – individual in population always represent a single problem solution. Binary chromosome is built of binary genes (0 or 1, true or false), this kind of representation make operators implementation much easier, but also has some disadvantages
Every genetic algorithm is built from 4 basic phases: crossover (expanding the population), mutation, evaluation and selection repeated in loop. There are many other steps that can be added and variations, but the classic genetic algorithm contains only that four. I also explain a basic terms from GA world.
Genetic algorithms (metaheuristics algorithms family) help us solve problems which are impossible, very hard or take too long to solve by other solutions.