Multi-Objective Optimization Using Evolutionary Algorithms¶
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
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.
Supported in partVerified against the publisher's abstract
Publisher's abstract supports EA-based multiobjective optimization generally, but not the claim's weighted-sum limits, Pareto-efficiency distinction, or named frontiers.
“Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.”
- 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¶
- Multi-Pressure Tradeoff Matrix
- It stops one loud pressure from silently dominating, replaces "is this variant good?" with "good on which pressure, at what cost to the others?", and gives a Pareto picture on which a tradeoff can be chosen rather than stumbled into.
This sourcePresents Pareto-optimal solution sets that expose tradeoffs among competing objectives for an explicit choice.
- It stops one loud pressure from silently dominating, replaces "is this variant good?" with "good on which pressure, at what cost to the others?", and gives a Pareto picture on which a tradeoff can be chosen rather than stumbled into.
- Pareto Frontier Analysis
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Read against the text for 1 of 3 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:0c0fcc3728ce · see in the full table