Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem extends the familiar Knapsack Problem by assigning values not only to individual objects but also to pairs of objects. In these problems, an object's value density is the sum of the values associated with it divided by its weight. Two greedy heuristics for the quadratic problem examine objects for inclusion in the knapsack in descending order of their value densities. Two genetic algorithms encode candidate selections of objects as binary strings and generate only strings whose selections of objects have total weight no more than the knapsack's capacity. One GA is naive; its operators apply no information about the values associated with objects. The second extends the naive GA with greedy techniques from the non-evolutionary heuristics. Its operators examine objects for inclusion in the knapsack in orders determined by tournaments based on objects' value densities. All four algorithms are tested on twenty problem instances whose optimum knapsack values are known. The greedy heuristics do well, as does the naive GA, but the greedy GA exhibits the best performance. In repeated trials on the test instances, it identifies optimum solutions more than nine times out of every ten.
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