Inheritance (genetic algorithm)
In genetic algorithms, inheritance is the ability of modeled objects to mate, mutate and propagate their problem solving genes to the next generation, in order to produce an evolved solution to a particular problem. The decision of which objects will be inherited from in each successive generation is determined by a fitness function. [1]
The propagation of traits between generations is similar to the inheritance of traits between generations of biological organisms. This process can also be viewed as a form of reinforcement learning, because the evolution of the objects is driven by the passing of traits from successful objects which can be viewed as a reward for their success, thereby promoting beneficial traits. [1]
Process
The traits of these objects can be thought of as their genes, in a form comparable to chromosomes, and they are passed on through a means analogous to biological reproduction. Once a new generation is ready to be created, all of the individuals that have been successful and chosen for reproduction are randomly paired together. Then, the traits of these individuals are passed on through a combination of crossover and mutation. [1]
References
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