Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem

• URL: http://arxiv.org/abs/2508.07077v1
• Date: Sat, 09 Aug 2025 19:04:12 GMT
• Title: Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
• Authors: Gustavo V. Nascimento, Ivan R. Meneghini, Valéria Santos, Eduardo Luz, Gladston Moreira,
• Abstract summary: This paper introduces an approach that directly integrates a Hamming distance-based measure of uniformity into the selection mechanism of a MOEA.<n> Experiments on a multi-objective formulation of the diet problem demonstrate that our approach significantly improves decision space diversity.<n>The proposed method offers a generalizable strategy for integrating decision space awareness into MOEAs.
• Score: 0.0699049312989311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost and nutritional content, is crucial. However, most MOEAs focus on optimizing solutions in the objective space, often neglecting the diversity of solutions in the decision space, which is critical for providing decision-makers with a wide range of choices. This paper introduces an approach that directly integrates a Hamming distance-based measure of uniformity into the selection mechanism of a MOEA to enhance decision space diversity. Experiments on a multi-objective formulation of the diet problem demonstrate that our approach significantly improves decision space diversity compared to NSGA-II, while maintaining comparable objective space performance. The proposed method offers a generalizable strategy for integrating decision space awareness into MOEAs.

Related papers

• MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning [68.91090643731987]
  Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems.<n>Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective.<n>We propose MO-mix to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem.
  arXiv  Detail & Related papers  (2026-02-28T16:25:22Z)
• AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization [61.535567824938205]
  We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem.<n>AdaEvolve consistently outperforms the open-ended baselines across 185 different open-ended optimization problems.
  arXiv  Detail & Related papers  (2026-02-23T18:45:31Z)
• Maximum Dispersion, Maximum Concentration: Enhancing the Quality of MOP Solutions [0.08192907805418582]
  Multi-objective optimization problems (MOPs) often require a trade-off between conflicting objectives, maximizing diversity and convergence in the objective space.<n>This study presents an approach to improve the quality of MOP solutions by optimizing the dispersion in the decision space and the convergence in a specific region of the objective space.
  arXiv  Detail & Related papers  (2025-06-27T18:32:22Z)
• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization.<n>Our guidance is a preference model trained to predict the probability that one design dominates another.<n>Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization [20.621635722585502]
  We show that a simple method that considers diversity of solutions in the solution space can benefit the search in evolutionary algorithms (EAs) We prove that the proposed method, working with crossover, can help enhance the exploration, leading to multimodal or even exponential acceleration on the expected running time.
  arXiv  Detail & Related papers  (2024-06-04T17:52:14Z)
• Solving the Food-Energy-Water Nexus Problem via Intelligent Optimization Algorithms [46.48853432592689]
  Food-Energy-Water systems are intricately linked among food, energy and water that impact each other. They usually involve a huge number of decision variables and many conflicting objectives to be optimized. In this paper, we solve a Food-Energy-Water optimization problem by using the state-of-art intelligent optimization methods and compare their performance.
  arXiv  Detail & Related papers  (2024-04-10T06:19:19Z)
• Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment [46.44464839353993]
  We introduce Rewards-in-Context (RiC), which conditions the response of a foundation model on multiple rewards in its prompt context. RiC only requires supervised fine-tuning of a single foundation model and supports dynamic adjustment for user preferences during inference time.
  arXiv  Detail & Related papers  (2024-02-15T18:58:31Z)
• A Fitness-assignment Method for Evolutionary Constrained Multi-objective Optimization [5.757908778750608]
  We propose an effective single-population fitness assignment-based CMOEA referred to as IcSDE+.<n>IcSDE+ is an efficient fusion of constraint violation (c), Shift-based Density Estimation (SDE), and sum of objectives (+)<n>The performance of IcSDE+ is favorably compared against 9 state-of-the-art CMOEAs on 6 different benchmark suites with diverse characteristics.
  arXiv  Detail & Related papers  (2023-05-30T04:19:01Z)
• An Analysis of Phenotypic Diversity in Multi-Solution Optimization [118.97353274202749]
  We show that multiobjective optimization does not always produce much diversity, multimodal optimization produces higher fitness solutions, and quality diversity is not sensitive to genetic neutrality. An autoencoder is used to discover phenotypic features automatically, producing an even more diverse solution set with quality diversity.
  arXiv  Detail & Related papers  (2021-05-10T10:39:03Z)
• Niching Diversity Estimation for Multi-modal Multi-objective Optimization [9.584279193016522]
  Niching is an important and widely used technique in evolutionary multi-objective optimization. In MMOPs, a solution in the objective space may have multiple inverse images in the decision space, which are termed as equivalent solutions. A general niching mechanism is proposed to make standard diversity estimators more efficient when handling MMOPs.
  arXiv  Detail & Related papers  (2021-01-31T05:23:31Z)
• Hybrid Adaptive Evolutionary Algorithm for Multi-objective Optimization [0.0]
  This paper proposed a new Multi-objective Algorithm as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA) called MoHAEA. MoHAEA is compared with four states of the art MOEAs, namely MOEA/D, pa$lambda$-MOEA/D, MOEA/D-AWA, and NSGA-II.
  arXiv  Detail & Related papers  (2020-04-29T02:16:49Z)
• GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction [69.94831587339539]
  We present a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions.
  arXiv  Detail & Related papers  (2020-02-17T20:21:20Z)

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.