The network shown in Fig. 3.1 is composed of linear neurons. The numbers at the
ID: 95557 • Letter: T
Question
The network shown in Fig. 3.1 is composed of linear neurons. The numbers at the connections indicate the values of the connection weights. a) Compute the output of the hidden-layer and the output-layer neurons for the input (0.5, 1). b) What is the output of the network for an input of (1, 2)? Do you have to do all the network computations once again in order to answer this question? Explain why you do or do not have to do the same. c) Discuss the effect of population size and mutation rate on the performance of a genetic algorithm.Explanation / Answer
3. a) Answer is (-2,3).
3. b) It's (-4,6). We do not have to do all the computations, because every neuron computes a linear function on its inputs, which means that the entire network computes a linear function. For such a function, if we double the input, we simply double the output as well.
3. C) The study of Genetic Algorithms(GAs) with finite population size requires the stochastic treatment of evolution.
1. In this study, we examined the effects of genetic fluctuations on the performance of GA calculations.
2. We considered the roles of mutation by using the stochastic schema theory within the framework of the Wright-Fisher model of Markov processes.
3. The success probability of obtaining the optimum solution was investigated experimentally and theoretically.
4. We noticed that mutation has effects of increasing the success probabilities. We also noticed crossover brings the population a good effect in results of GA.
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