Evolutionary Diversity Optimisation in Constructing Satisfying Assignments

• URL: http://arxiv.org/abs/2305.11457v1
• Date: Fri, 19 May 2023 06:26:10 GMT
• Title: Evolutionary Diversity Optimisation in Constructing Satisfying Assignments
• Authors: Adel Nikfarjam and Ralf Rothenberger and Frank Neumann and Tobias Friedrich
• Abstract summary: This paper studies the Boolean satisfiability problem (SAT) in the context of EDO. SAT is of great importance in computer science and differs from the other problems studied in EDO literature, such as KP and TSP. We introduce evolutionary algorithms employing a well-known SAT solver to maximise diversity among a set of SAT solutions explicitly.
• Score: 20.386139395296215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satisfiability problem (SAT) in the context of EDO. SAT is of great importance in computer science and differs from the other problems studied in EDO literature, such as KP and TSP. SAT is heavily constrained, and the conventional evolutionary operators are inefficient in generating SAT solutions. Our approach avails of the following characteristics of SAT: 1) the possibility of adding more constraints (clauses) to the problem to forbid solutions or to fix variables, and 2) powerful solvers in the literature, such as minisat. We utilise such a solver to construct a diverse set of solutions. Moreover, maximising diversity provides us with invaluable information about the solution space of a given SAT problem, such as how large the feasible region is. In this study, we introduce evolutionary algorithms (EAs) employing a well-known SAT solver to maximise diversity among a set of SAT solutions explicitly. The experimental investigations indicate the introduced algorithms' capability to maximise diversity among the SAT solutions.

Related papers

• Self-Satisfied: An end-to-end framework for SAT generation and prediction [0.7340017786387768]
  We introduce hardware accelerated algorithms for fast SAT problem generation and a geometric SAT encoding. These advances allow us to scale our approach to SAT problems with thousands of variables and tens of thousands of clauses. A fundamental aspect of our work concerns the very nature of SAT data and its suitability for training machine learning models.
  arXiv  Detail & Related papers  (2024-10-18T22:25:54Z)
• GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection [45.222591894755105]
  We present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances. We enrich the graph representation with domain-specific decisions, such as novel node feature design. We demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime.
  arXiv  Detail & Related papers  (2024-05-17T18:00:50Z)
• General Method for Solving Four Types of SAT Problems [12.28597116379225]
  Existing methods provide varying algorithms for different types of Boolean satisfiability problems (SAT) This study proposes a unified framework DCSAT based on integer programming and reinforcement learning (RL) algorithm to solve different types of SAT problems.
  arXiv  Detail & Related papers  (2023-12-27T06:09:48Z)
• Decomposing Hard SAT Instances with Metaheuristic Optimization [52.03315747221343]
  We introduce the notion of decomposition hardness (d-hardness) We show that the d-hardness expresses an estimate of the hardness of $C$ w.r.t.
  arXiv  Detail & Related papers  (2023-12-16T12:44:36Z)
• SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving [64.38649623473626]
  Large Language Models (LLMs) have driven substantial progress in artificial intelligence. We propose a novel framework called textbfSEquential subtextbfGoal textbfOptimization (SEGO) to enhance LLMs' ability to solve mathematical problems.
  arXiv  Detail & Related papers  (2023-10-19T17:56:40Z)
• Addressing Variable Dependency in GNN-based SAT Solving [19.38746341365531]
  We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. Experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets.
  arXiv  Detail & Related papers  (2023-04-18T05:33:33Z)
• Estimating the hardness of SAT encodings for Logical Equivalence Checking of Boolean circuits [58.83758257568434]
  We show that the hardness of SAT encodings for LEC instances can be estimated textitw.r.t some SAT partitioning. The paper proposes several methods for constructing partitionings, which, when used in practice, allow one to estimate the hardness of SAT encodings for LEC with good accuracy.
  arXiv  Detail & Related papers  (2022-10-04T09:19:13Z)
• SATformer: Transformer-Based UNSAT Core Learning [0.0]
  This paper introduces SATformer, a Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the problem from the opposite direction by focusing on unsatisfiability. SATformer is trained through a multi-task learning approach, using the single-bit satisfiability result and the minimal unsatisfiable core. Experimental results show that our SATformer can decrease the runtime of existing solvers by an average of 21.33%.
  arXiv  Detail & Related papers  (2022-09-02T11:17:39Z)
• DeepSAT: An EDA-Driven Learning Framework for SAT [9.111341161918375]
  We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions.
  arXiv  Detail & Related papers  (2022-05-27T03:20:42Z)
• Machine Learning Methods in Solving the Boolean Satisfiability Problem [72.21206588430645]
  The paper reviews the recent literature on solving the Boolean satisfiability problem (SAT) with machine learning techniques. We examine the evolving ML-SAT solvers from naive classifiers with handcrafted features to the emerging end-to-end SAT solvers such as NeuroSAT.
  arXiv  Detail & Related papers  (2022-03-02T05:14:12Z)
• Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis [63.53283025435107]
  CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. In this work, we propose a one-shot model derived from the Transformer architecture to solve the MaxSAT problem.
  arXiv  Detail & Related papers  (2021-07-15T04:47:35Z)

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.