Randomized Optimization Algorithms

Developed randomized hill-climbing, simulated annealing, genetic algorithm, and mutual-information-maximizing in-put clustering (MIMIC) algorithms to solve optimization problems (travel salesman, knapsack, and four peaks) [Python, mlrose]

Used optimization algorithms to optimize neural network’s weights, and compared performance of the developed network with a regular neural network trained using backpropagation