The latest Optimize with GA & RL certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Optimize with GA & RL exam and earn Optimize with GA & RL certification.
Question 1
Which advantages do genetic algorithms provide over linear programming for inventory optimization? (Select all that apply)
A. Handle discrete and combinatorial decision variables such as supplier selection or batch sizing
B. Process non-linear cost relationships such as quantity discounts or stepwise holding costs
C. Guarantee mathematically optimal solutions
D. Manage multiple conflicting objectives (e.g., cost minimization vs service level maximization)
E. Incorporate soft constraints using penalty functions (e.g., service-level violations)
Correct Answer
A. Handle discrete and combinatorial decision variables such as supplier selection or batch sizing
B. Process non-linear cost relationships such as quantity discounts or stepwise holding costs
D. Manage multiple conflicting objectives (e.g., cost minimization vs service level maximization)
E. Incorporate soft constraints using penalty functions (e.g., service-level violations)
Question 2
When comparing genetic algorithm results to linear programming baseline for inventory problems, what indicates the genetic algorithm is performing effectively?
A. Genetic algorithm finds solutions faster than linear programming
B. Genetic algorithm produces identical solutions to linear programming
C. Genetic algorithm handles problem constraints that linear programming cannot address
D. Genetic algorithm achieves lower total costs or higher service levels than linear programming baseline
Correct Answer
D. Genetic algorithm achieves lower total costs or higher service levels than linear programming baseline
Question 3
You’re implementing a genetic algorithm for inventory optimization across 10 products using a variable (s, Q) replenishment policy. What should each chromosome contain to properly represent a complete inventory solution?
A. 10 genes representing only reorder points for each product
B. 20 genes representing reorder points and order quantities for each product
C. 1 gene representing the total inventory investment across all products
D. 100 genes representing detailed daily inventory levels for each product
Correct Answer
B. 20 genes representing reorder points and order quantities for each product
Question 4
A genetic algorithm population contains 50 chromosomes. If you use tournament selection with a tournament size of 3, how many random selections occur to choose parents for one crossover operation?
Correct Answer
6
Question 5
In genetic algorithms for inventory problems, what term describes the function that evaluates how well each solution performs against optimization criteria?
Correct Answer
Fitness function