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An Evolutionary Algorithm for Finding Efficient Solutions in Multi-Attribute Auctions

    There is a growing interest in electronic auctions. Many researchers consider a single-attribute, although auctions are multi-attribute in nature in practice. Addressing multiple attributes increases the difficulty of the problem substantially. We develop an evolutionary algorithm (EA) for multi-attribute multi-item reverse auctions. We try to generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and report our findings.


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