i. Suppose you are using an evolutionary algorithm to optimize a ONE MAX fitness function (this fitness function assigns fitness values to bit strings according to the number of 1 values they contain) and one-point crossover is applied to the following two bit string chromosomes: 1101010 and 1000110 What is the fittest possible individual that could be produced by such a crossover? What crossover point would be needed to produce this individual? Show the two children that would be produced for this crossover point and give their fitness values. Page 4 of 5 ii. For this ONE MAX fitness function, would applying one-point crossover to high-fitness parents likely also produce children with high fitness values? Explain your answer. iii. Suppose you wanted to use an evolutionary algorithm to evolve bit strings that had either as many 1s or as many 0 s as possible, i.e. very fit strings would be strings composed either of mostly 1s or otherwise strings composed of mostly 0s. Describe a suitable fitness function that could be used in this situation. iv. For this fitness function, would applying one-point crossover to high-fitness individuals always likely produce children with high fitness values? Explain your answer. (6 marks) (b) The following table gives a genetic algorithm population along with the fitness values of its members. Use roulette wheel selection to select two individuals from this population. If you need uniform random numbers in part (b) or part (c) choose them from the following list: 0.9123,0.4256,0.1223,0.6579,0.2198,0.8875,0.2130,0.3168,0.7789,0.0001 and repeat from the beginning if you need more. Keep all calculations to four digits of precision