Evolutionary multimodal optimization (EMO) refers to a class of optimization techniques that are designed to identify multiple optimal solutions (or "modes") in a problem landscape, particularly when that landscape is complex, multimodal, or has many local optima. Traditional optimization methods often focus on finding a single optimal solution, but in many real-world scenarios, obtaining a diverse set of good solutions is valuable.
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