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A grid-based evolutionary algorithm for many-objective optimization

<22> Published in a journal ranked 1/114 in Computer Science (Artificial Intelligence) according to 5-year Impact Factor (6.226) in ISI Web of Knowledge, the paper proposes a grid-based evolutionary algorithm (GrEA) to solve challenging many-objective optimization problems via the introduction of two novel concepts: grid dominance and grid difference, and three grid-based criteria: ranking, crowding distance, and coordinate point distance. The extensive experiments show that GrEA is more efficient than many state-of-the-art algorithms. Although only published in October 2013, the paper has generated much interest, e.g., there have been a number of requests for source code already.