Interactive Evolutionary Multi-Objective Optimization via
Learning-to-Rank
- URL: http://arxiv.org/abs/2204.02604v1
- Date: Wed, 6 Apr 2022 06:34:05 GMT
- Title: Interactive Evolutionary Multi-Objective Optimization via
Learning-to-Rank
- Authors: Ke Li, Guiyu Lai, Xin Yao
- Abstract summary: This paper develops a framework for designing preference-based EMO algorithms to find solution(s) of interest (SOI) in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates.
By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm.
- Score: 8.421614560290609
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In practical multi-criterion decision-making, it is cumbersome if a decision
maker (DM) is asked to choose among a set of trade-off alternatives covering
the whole Pareto-optimal front. This is a paradox in conventional evolutionary
multi-objective optimization (EMO) that always aim to achieve a well balance
between convergence and diversity. In essence, the ultimate goal of
multi-objective optimization is to help a decision maker (DM) identify
solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple
conflicting criteria. Bearing this in mind, this paper develops a framework for
designing preference-based EMO algorithms to find SOI in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several
iterations, the DM is invited to elicit her feedback with regard to a couple of
incumbent candidates. By collecting such information, her preference is
progressively learned by a learning-to-rank neural network and then applied to
guide the baseline EMO algorithm. Note that this framework is so general that
any existing EMO algorithm can be applied in a plug-in manner. Experiments on
$48$ benchmark test problems with up to 10 objectives fully demonstrate the
effectiveness of our proposed algorithms for finding SOI.
Related papers
- UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [75.11267478778295]
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours.
We focus on the case of linear utility functions parameterised by weight vectors w.
We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
arXiv Detail & Related papers (2024-05-01T09:34:42Z) - Data-Efficient Interactive Multi-Objective Optimization Using ParEGO [6.042269506496206]
Multi-objective optimization seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives.
In practical applications, decision-makers (DMs) will select a single solution that aligns with their preferences to be implemented.
We propose two novel algorithms that efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems.
arXiv Detail & Related papers (2024-01-12T15:55:51Z) - Multi-Objective Bayesian Optimization with Active Preference Learning [18.066263838953223]
We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in a multi-objective optimization (MOO) problem.
To minimize the interaction cost with the decision maker (DM), we also propose an active learning strategy for the preference estimation.
arXiv Detail & Related papers (2023-11-22T15:24:36Z) - Rethinking and Benchmarking Predict-then-Optimize Paradigm for
Combinatorial Optimization Problems [62.25108152764568]
Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
We consider the performance of prediction and decision-making in a unified system.
We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
arXiv Detail & Related papers (2023-11-13T13:19:34Z) - Interactive Hyperparameter Optimization in Multi-Objective Problems via
Preference Learning [65.51668094117802]
We propose a human-centered interactive HPO approach tailored towards multi-objective machine learning (ML)
Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator.
arXiv Detail & Related papers (2023-09-07T09:22:05Z) - Multi-Objective GFlowNets [59.16787189214784]
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization.
In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives.
We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse optimal solutions, based on GFlowNets.
arXiv Detail & Related papers (2022-10-23T16:15:36Z) - Data-Driven Evolutionary Multi-Objective Optimization Based on
Multiple-Gradient Descent for Disconnected Pareto Fronts [6.560512252982714]
This paper proposes a data-driven evolutionary multi-objective optimization (EMO) algorithm based on multiple-gradient descent.
Its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations.
arXiv Detail & Related papers (2022-05-28T06:01:41Z) - Result Diversification by Multi-objective Evolutionary Algorithms with
Theoretical Guarantees [94.72461292387146]
We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA)
We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$.
When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
arXiv Detail & Related papers (2021-10-18T14:00:22Z) - Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems.
Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC.
We provide a practical parametrization of the core optimization problem.
arXiv Detail & Related papers (2021-07-08T18:01:02Z) - Decomposition-Based Multi-Objective Evolutionary Algorithm Design under
Two Algorithm Frameworks [7.745468825770201]
We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework.
The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.
arXiv Detail & Related papers (2020-08-17T05:28:20Z) - Algorithm Configurations of MOEA/D with an Unbounded External Archive [7.745468825770201]
We show that the performance of MOEA/D is improved by linearly changing the reference point specification during its execution.
We also examine the use of a genetic algorithm-based offline hyper-heuristic method to find the best configuration of MOEA/D in each framework.
arXiv Detail & Related papers (2020-07-27T08:14:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.