Efficient anytime algorithms to solve the bi-objective Next Release Problem

• URL: http://arxiv.org/abs/2402.04586v1
• Date: Wed, 7 Feb 2024 05:03:31 GMT
• Title: Efficient anytime algorithms to solve the bi-objective Next Release Problem
• Authors: Miguel \'Angel Dom\'inguez-R\'ios, Francisco Chicano, Enrique Alba, Isabel Mar\'ia del \'Aguila, Jos\'e del Sagrado
• Abstract summary: Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. We propose five new methods that maintain a well-spread set of solutions at any time during the search.
• Score: 2.8148957592979422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. The selection should be done in a way that maximizes the satisfaction of the stakeholders while the development cost is minimized and the constraints of the requirements are fulfilled. Recent works have solved the problem using exact methods based on Integer Linear Programming. In practice, there is no need to compute all the efficient solutions of the problem; a well-spread set in the objective space is more convenient for the decision maker. The exact methods used in the past to find the complete Pareto front explore the objective space in a lexicographic order or use a weighted sum of the objectives to solve a single-objective problem, finding only supported solutions. In this work, we propose five new methods that maintain a well-spread set of solutions at any time during the search, so that the decision maker can stop the algorithm when a large enough set of solutions is found. The methods are called anytime due to this feature. They find both supported and non-supported solutions, and can complete the whole Pareto front if the time provided is long enough.

Related papers

• Learning Multiple Initial Solutions to Optimization Problems [52.9380464408756]
  Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications. We propose learning to predict emphmultiple diverse initial solutions given parameters that define the problem instance. We find significant and consistent improvement with our method across all evaluation settings and demonstrate that it efficiently scales with the number of initial solutions required.
  arXiv  Detail & Related papers  (2024-11-04T15:17:19Z)
• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• PMGDA: A Preference-based Multiple Gradient Descent Algorithm [12.600588000788214]
  It is desirable in many multi-objective machine learning applications, such as multi-task learning, to find a solution that fits a given preference of a decision maker. This paper proposes a novel predict-and-correct framework for locating a solution that fits the preference of a decision maker.
  arXiv  Detail & Related papers  (2024-02-14T11:27:31Z)
• Multi-objective QUBO Solver: Bi-objective Quadratic Assignment [0.0]
  We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver. The proposed multi-objective DA algorithm is validated on the bi-objective Quadratic Assignment Problem.
  arXiv  Detail & Related papers  (2022-05-26T14:48:03Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)
• A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering [60.768146126094955]
  Weakly supervised question answering usually has only the final answers as supervision signals. There may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance. We propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions.
  arXiv  Detail & Related papers  (2021-06-14T05:47:41Z)
• Instance Specific Approximations for Submodular Maximization [45.91235224228292]
  We look for methods to benchmark the performance of algorithms against optimal solutions on real-world instances. A major question is how to measure the performance of an algorithm in comparison to an optimal solution on instances we encounter in practice. Our main contribution is not a new algorithm for submodular minimization but an analytical method that measures how close an algorithm for submodular instance is to optimal.
  arXiv  Detail & Related papers  (2021-02-23T19:39:32Z)
• Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization [7.745468825770201]
  We discuss subset selection from a viewpoint of the final decision making. We show that the formulated function is the same as the IGD plus indicator.
  arXiv  Detail & Related papers  (2020-06-15T06:26:58Z)
• sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission Planning Problems [2.191505742658975]
  Some problems have many objectives which lead to a large number of non-dominated solutions. This paper presents a new algorithm that has been designed to obtain the most significant solutions. This new algorithm has been applied to the real world application in Unmanned Air Vehicle (UAV) Mission Planning Problem.
  arXiv  Detail & Related papers  (2020-02-20T17:07:08Z)
• Pareto Multi-Task Learning [53.90732663046125]
  Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. It is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
  arXiv  Detail & Related papers  (2019-12-30T08:58:40Z)

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.