Genetic Algorithm (GA) & Particle Swarm Optimization - IT Assignment Help

Assignment Task

Task

Part  [] This part requires for the student to obtain a little bit of self-familiarisation with some existing standard libraries (just to avoid implementing the evolutionary optimisers from scratch). Therefore, use a tool-box/library/package for the platform of your choice (various options are given below) to create a genetic algorithm (GA) and a particle swarm optimisation (PSO) to train the multilayer neural network from the previous Part 2. The GA and PSO optimisers should search for the best weights for the network to solve a simple problem (such as one from Part 2, or any different classification/regression problem of your choice). Only the forward pass and the overall output error need to be evaluated for the objective function of the GA and the PSO. For the GA, you can use any type of chromosome encoding you like (binary or real-valued, or both!) and any fitness scaling procedure (scaling/ranking), or genetic crossover/mutation operator you think is the most efficient for your problem. Try to briefly justify your decisions for choosing the specific mechanisms or operators. Compare a few different options your library supports (e.g., different popula-tion/swarm sizes, generations, mutation/crossover rates, stopping criteria or whatever is supported by the implementation you use). Include a design section that explains how you designed your evolutionary-based MLP training, its main components, alternatives, and how you tested them. Extra marks will be given for experimentations with different GA and PSO operators. You can also experiment optionally with any other evolutionary optimisation method (other than PSO or GA) your library supports. Feel free to use whatever your libraries support, but please, explain what they do. 
 

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