Optimizations of Stalagmite function using GA

Genetic Algorithm is an optimization technique based on Charles Darwins famous evolution theory of \'Survival of the Fittest\'. Its based on random yet structured evaluation of a function through several iterations, evolving after each iteration to get a tad bit closer to the solution. They are not fool proof and do not guarantee an objective optimized solution. However, they are proven to produce relatively \'good results\'. They function of choosing a random value in the beginning from the function domain as specified. After checking the value of the function at that chosen value does the algorithm kick starts its evolution.

It proceeds in these logical steps namely:-

1)selection technique

2)crossover

3)mutation

The selection technique first choses two \'parents\' which are used to produce two offspring value using the crossover technique. The crossover operator (main searching tool) converts the parent pair into binary values and swaps between randomely selected \'cross-over\' points to form the offspring pair.

The mutation algorithm or operator whose value is pre-meditated before the ga function starts changes certain fragments from the offspring pair to alter or evolve its value.

Then there is the elitist operator that keeps a check on GA algorithm that it does not get worse or exacerbate over time.

ALL IS DONE AFTER CONVERTING VALUES INTO BINARY CODES AND PERFORMING OPERATIONS ON ITS BINARY VALUE.

After every conversion, the value is checked through the specified function and after each iteration the function is found to coverge to an optimized value.

ONE MIGHT HAVE TO ALTER THE GA FUNCTION DEPENDING UPON THE REQUIREMENT TO GET THE DESIRED RESULT AS ITS DOES NOT GUARANTEE CONVERGENCE NOR OBJECTIVE OPTIMIZATION AS MENTIONED EARLIER.

Here you will find the program of GA challenge i.e. optimizing Stalagmite function using GA.

https://drive.google.com/drive/folders/1ucnJ31jhjFPuHg2XwrF4czCixHkwPBS8?usp=sharing