Question: large financial advising company is using a genetic algorithm to investigate potential investment portfolios given a large set of constraints. In the following table ,

large financial advising company is using a genetic algorithm to investigate potential investment portfolios given a large set of constraints. In the following table, each row identifies a different potential investment portfolio and the column Portfolio identifies the 20 different hypothetical portfolios. Columns 2-11 represent investments that could be made, a 0 represents the investment was not included in the portfolio and a 1 represents that the investment was included in the portfolio. The column fitness rates each portfolio, taking into accountcost, risk, and projected return (the higher the fitness, the better the portfolio).

Pick two portfolios (from 1 to 20) that a genetic algorithm would likely choose for parents in the next iteration.

2. Combine the two parents you selected in 1 and suggest a new child portfolio, make the crossover between the parents at any point you would like, but explain what portion of each parent is in the child portfolio you create.

3. Make a mutation in your child. Explain what you did.

large financial advising company is using a genetic algorithm to investigate

netic Algorithm Assignment BDA MGMT 4073 Summer 23 arge financial advising company is using a genetic algorithn nvestigate potential investment portfolios given a large set istraints. In the following table, each row identifies a ferent potential investment portfolio and the column rtfolio" identifies the 20 different hypothetical portfolios. lumns 2-11 represent investments that could be made, a 0 resents the investment was not included in the portfolio and epresents that the investment was included in the portfolio. e column fitness rates each portfolio, taking into account t, risk, and projected return (the higher the fitness, the better portfolio). nsider the above table the initial generation for a genetic orithm, and answer the following questions: Pick two portfolios (from 1 to 20 ) that a genetic algorithm would likely choose for parents in the next iteration. 2. Combine the two parents you selected in 1 and suggest a new "child" portfolio, make the crossover between the parents at any point you would like, but explain what portion of each parent is in the child portfolio you create. Make a mutation in your child. Explain what you did

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