Multi-Objective Optimization Using Evolutionary Algorithms¶
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons.
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2 citations across 2 artifacts.
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Primes¶
- Multiobjective Optimization
- Not necessarily a weighted sum — while weighted-sum scalarization is the simplest approach, it has real limitations (cannot find non-convex-frontier points, requires pre-specified weights, collapses structure) and is appropriate only for some problem types. Not the same as Pareto efficiency in economics, though related — economic Pareto efficiency is a concept about allocations; multiobjective Pareto-optimal solutions generalize the idea to any decision problem with multiple criteria. Not a substitute for decision-maker judgment — multiobjective optimization characterizes the compromise space; it does not eliminate the need for human judgment to select among compromise options. Not automatically solvable at scale — problems with many objectives, nonconvex structure, or expensive objective evaluations pose distinct computational challenges; current research frontiers include many-objective optimization, expensive-black-box multiobjective optimization, and multiobjective reinforcement learning, with Deb (2001) charting the evolutionary-algorithm research program that drove much of this expansion.
This sourceFoundational treatment of evolutionary MOO; charts the research program (NSGA, NSGA-II, many-objective optimization, expensive-black-box methods) that drove the modern expansion of MOO solver capabilities.
- Not necessarily a weighted sum — while weighted-sum scalarization is the simplest approach, it has real limitations (cannot find non-convex-frontier points, requires pre-specified weights, collapses structure) and is appropriate only for some problem types. Not the same as Pareto efficiency in economics, though related — economic Pareto efficiency is a concept about allocations; multiobjective Pareto-optimal solutions generalize the idea to any decision problem with multiple criteria. Not a substitute for decision-maker judgment — multiobjective optimization characterizes the compromise space; it does not eliminate the need for human judgment to select among compromise options. Not automatically solvable at scale — problems with many objectives, nonconvex structure, or expensive objective evaluations pose distinct computational challenges; current research frontiers include many-objective optimization, expensive-black-box multiobjective optimization, and multiobjective reinforcement learning, with Deb (2001) charting the evolutionary-algorithm research program that drove much of this expansion.
Mechanisms¶
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