Please use python to write this program.

Part B. Local Search Algorithms (60 points) In this part you will implement stochastic optimization techniques. You will work individually and turn in commented code, runs on some test data to show the functions work, and a report with analysis of your results when you vary algorithm options or paramters. The stochastic optimization techniques you will implement and experiment with are the following: Simulated Annealing (SA): You will implement the single point approach of simulated annealing, as described in Figure 4.5, page 115 of the Russell & Norvig textbook (4th Edition). Genetic Algorithms (GA): You will implement the multi-point approach of genetic algorithms, as described in Figure 4.8, page 119 of the Russell & Norvig textbook (4th Edition). You will evaluate the performance of the algorithms on some difficult test problems, provided as part of the assignment, as well as other problems of varying degree of difficulty in terms of the number of local optima, function smoothness, etc. of your choice. You are required to vary the algorithm parameters, such as annealing schedule in SA and crossover/mutation rates in GA. You are also encouraged to try variants of these algorithms, such as no crossover in GAs. Use bit-string representations of suitable size and map bit strings to real values in the range of functions (some code provided for this). Part B. Local Search Algorithms (60 points) In this part you will implement stochastic optimization techniques. You will work individually and turn in commented code, runs on some test data to show the functions work, and a report with analysis of your results when you vary algorithm options or paramters. The stochastic optimization techniques you will implement and experiment with are the following: Simulated Annealing (SA): You will implement the single point approach of simulated annealing, as described in Figure 4.5, page 115 of the Russell & Norvig textbook (4th Edition). Genetic Algorithms (GA): You will implement the multi-point approach of genetic algorithms, as described in Figure 4.8, page 119 of the Russell & Norvig textbook (4th Edition). You will evaluate the performance of the algorithms on some difficult test problems, provided as part of the assignment, as well as other problems of varying degree of difficulty in terms of the number of local optima, function smoothness, etc. of your choice. You are required to vary the algorithm parameters, such as annealing schedule in SA and crossover/mutation rates in GA. You are also encouraged to try variants of these algorithms, such as no crossover in GAs. Use bit-string representations of suitable size and map bit strings to real values in the range of functions (some code provided for this)