Abstract
Evolutionary computation (EC) algorithms involve a careful collaborative and iterative update of a population of solutions to reach near a desired target. In a single-objective search and optimization problem, the respective optimal solution is often the single target. In a multi-criterion optimization problem, the target is a set of Pareto-optimal solutions. Although EC field started with solving single-objective problems, EC researchers soon realized that they were ideal for finding a well-diversed set of multiple Pareto-optimal solutions simultaneously for multi-criterion optimization problems, thereby making a clear niche of EC algorithms compared to their point-based classical counterparts. In this keynote talk, we provide a brief chronology of events on the evolutionary multi-criterion optimization (EMO) field in the past almost three decades, key challenges it faced, and key events and publications which pushed the field forward. We shall also provide a brief account of the current activities and a few pertinent future areas of research and applications.
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