AI‐based computational methods are capable of obtaining satisfactory solutions for otherwise intractable problems if a proper model can be constructed for the problem. In this chapter, we study a well‐known computationally expensive problem, namely the so‐called knapsack problem (KP)1[109], and present a computationally feasible solution using genetic algorithms. KP is designed specifically to solve the types of challenges where decisions are choices that must obey a set of limitations with the goal of maximizing the outcome of a task.
In the engineering domain, the KP represents a class of optimization problems where there are multiple and often conflicting requirements to be selected to satisfy objectives such as to maximize a profit or minimize a loss. Airlines and naval cargo ships have to select freight to maximize their profits while staying within the weight and capacity limitations of a given flight. A portfolio manager considers potential investment opportunities and their expected returns to maximize gains without exceeding their allocated budget. A project manager may need to select a team from a pool of engineers with different levels of expertise and salaries, while including individuals with the highest talent level and keeping the total cost as low as possible.
Figure 15.1Example menu items for a fictitious ...
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