Inheritance genetic algorithm

In genetic algorithms, inheritance allows modeled objects to mate, mutate, and propagate their problem-solving genes to produce evolved solutions. The selection of objects for each generation is determined by a fitness function specific to the problem. Traits are passed through chromosomes, represented as binary numbers, similar to biological reproduction.

The process begins with pairing successful objects, randomly determining a crossover point, and swapping genes after this point. Mutation is then applied randomly to the child objects. This cycle repeats until a desired generation or solution is achieved. Other genetic operators like regrouping exist but are less common.

For example, two parent objects with bit strings undergo crossover at a random point, swapping their bits post-crossover. Mutation then flips specific bits, resulting in new child objects. This process mimics reinforcement learning by rewarding successful traits and promoting beneficial evolution across generations.