To demonstrate the use genetic algorithm for finding the optimal path.
Link to the Video!!
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems.
Step 1: Initialize. Create a population of N elements, each with randomly generated DNA.
Step 2: Selection. Evaluate the fitness of each element of the population and build a mating pool.
Step 3: Reproduction. Repeat N times:
a) Pick two parents with probability according to relative fitness.
b) Crossover—create a “child” by combining the DNA of these two parents.
c) Mutation—mutate the child’s DNA based on a given probability.
d) Add the new child to a new population.
Step 4. Replace the old population with the new population and return to Step 2.
The basic goal of the rockets is to land at the goal position without colliding into the obstacles. Here you will observe that the rockets evolve with time and are able to find their path to the goal position in relatively very less amout time.
