Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference最新文献

Phase-Alternation and Tree-Initialization to Facilitate Interpretable Rule-based Machine Learning. 相位交替和树初始化促进可解释的基于规则的机器学习。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2026-01-01 Epub Date: 2026-07-10 DOI: 10.1145/3795095.3805127

Gabriel Lipschutz-Villa, Harsh Bandhey, Khoi Dinh, Michael Heider, Malek Kamoun, Ryan J Urbanowicz

{"title":"Phase-Alternation and Tree-Initialization to Facilitate Interpretable Rule-based Machine Learning.","authors":"Gabriel Lipschutz-Villa, Harsh Bandhey, Khoi Dinh, Michael Heider, Malek Kamoun, Ryan J Urbanowicz","doi":"10.1145/3795095.3805127","DOIUrl":"10.1145/3795095.3805127","url":null,"abstract":"Interpretable machine learning is important for scientific, biomedical, and other high-stakes domains where transparency, trust, and human understanding of models are required. Recently, an evolutionary rule-based machine learning algorithm called HEROS was proposed, which separated rule from rule-set discovery each with distinct multi-objective optimization strategies yielding a two-phase, noise agnostic framework that found accurate, highly compact, and interpretable solutions on a diverse set of challenging benchmarks without requiring hyperparameter optimizations. However, HEROS is currently limited by its strictly sequential two-phase design which requires committing in advance to a fixed number of rule-discovery iterations. This work extends HEROS with a phase alternation scheme interleaving rule discovery and rule-set optimization. This enables refinement of both tasks throughout training in an 'anytime-algorithm' manner. We also introduce a tree-based rule initialization strategy to accelerate early-stage rule discovery. These extensions aim to preserve the empirical performance of the original HEROS framework under a similar computational budget while providing greater flexibility for algorithm application and problem scalability. Using the same diverse set of benchmark datasets, we evaluate and compare these HEROS extensions to the original HEROS algorithm and established rule-based learning systems including RIPPER and BioHEL. We demonstrate the advantages of this extended HEROS framework.","PeriodicalId":88876,"journal":{"name":"Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference","volume":"2026 ","pages":"300-309"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13528405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Audience-Customized Translation of Rule-Based Evidence with Large Language Models Across Multiplexer Benchmarks. 基于规则的证据与跨多路复用基准的大型语言模型的受众定制翻译。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2026-01-01 Epub Date: 2026-08-13 DOI: 10.1145/3795101.3814722

Harsh Bandhey, Gabriel Lipschutz-Villa, Khoi Dinh, Malek Kamoun, Ryan J Urbanowicz

{"title":"Audience-Customized Translation of Rule-Based Evidence with Large Language Models Across Multiplexer Benchmarks.","authors":"Harsh Bandhey, Gabriel Lipschutz-Villa, Khoi Dinh, Malek Kamoun, Ryan J Urbanowicz","doi":"10.1145/3795101.3814722","DOIUrl":"10.1145/3795101.3814722","url":null,"abstract":"Evolutionary rule-based machine learning (ERBML) algorithms can capture complex relationships while still yielding highly interpretable models comprised of IF:THEN rules. During prediction, 'matching' rules contribute to, and form the explanation for, the model's prediction. However, rules and their associated parameters (in their raw form) are likely too technical for their intended users. This study examines the feasibility of using a large language model (LLM) to translate the prediction evidence from matching rules into natural language text for different audiences, e.g. layman, clinician, expert. Using models trained by the 'HEROS' ERBML on MUX benchmarks, we evaluate LLM text quality metrics under different scenarios (i.e. 1,800 prediction explanations). We observe that (1), intuitively, LLM quality performance improves on HEROS models that have been more ideally trained, (2) making a glossary available to the LLM to define feature names generally raises explanation audience-fit scores and sometimes lowers overstatement (beyond rule-evidence), but it also lengthens explanations and often increases hallucination rate, and (3) audience customization creates some LLM performance trade-offs. These results suggest that constrained LLM translation of rules for natural language prediction explanations is feasible, while highlighting the importance of carefully designing the LLM prompts and evidence input from the ERBML.","PeriodicalId":88876,"journal":{"name":"Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference","volume":"2026 Companion","pages":"1175-1183"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13528409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling. 基于规则的机器学习：用于可解释噪声不可知建模的分离规则和规则集pareto优化。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2025-01-01 Epub Date: 2025-07-13 DOI: 10.1145/3712256.3726461

Gabriel Lipschutz-Villa, Harsh Bandhey, Ruonan Yin, Malek Kamoun, Ryan J Urbanowicz

{"title":"Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling.","authors":"Gabriel Lipschutz-Villa, Harsh Bandhey, Ruonan Yin, Malek Kamoun, Ryan J Urbanowicz","doi":"10.1145/3712256.3726461","DOIUrl":"10.1145/3712256.3726461","url":null,"abstract":"Rule-based machine learning (RBML) algorithms, e.g. learning classifier systems (LCSs), can capture complex relationships while yielding more interpretable models than most other machine learning algorithms. Traditional LCSs rely on a single fitness function for both rule and/or rule-set optimization. However, ideal rule vs. rule-set discovery often requires distinct and multiple objectives. Recently, hybrid-LCSs were proposed that explicitly separated the task of rule vs. rule-set discovery but relied on distinct single-objective or weighted multi-objective fitness functions. This study introduces a newly developed Heuristic Evolutionary Rule Optimization System (HEROS) that combines previous LCS innovations aimed at tackling noisy, larger-scale, classification tasks, while adopting separation of rule vs. rule-set evolution. Uniquely, HEROS employs a custom Pareto-front-based multi-objective fitness function (for rule discovery) and NSGA-II-style multi-objective optimization (for rule-set discovery) to solve both clean and noisy-signal classification problems agnostically. Rule discovery is driven by rule-accuracy and instance coverage objectives, while rule-set discovery is driven by prediction accuracy and rule-set size objectives. Using diverse simulated benchmark datasets, i.e. noisy (GAMETES) and clean (MUX), we demonstrate proof-of-principle that HEROS can directly discover accurate, highly-compact, interpretable, and ideal solutions when compared to the established 'ExSTraCS' RBML algorithm, without objective weightings or adjusting hyperparameters.","PeriodicalId":88876,"journal":{"name":"Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference","volume":"2025 ","pages":"407-415"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13528404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs 用复杂程序图寻找多任务视觉强化学习问题的简单解决方案

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_1

Caleidgh Bayer, Ryan Amaral, Robert J. Smith, Alexandru Ianta, M. Heywood

引用次数: 1

Evolution of the Semiconductor Industry, and the Start of X Law 半导体产业的演变和X定律的开始

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_11

A. Sloss

引用次数: 0

Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade 回到未来——十年后重新审视OrdinalGP和可信赖模型

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_7

M. Kotanchek, N. Haut

引用次数: 1

Fitness First 健康第一

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_8

W. Langdon

引用次数: 5

Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules) 基于模块提取与吸收的遗传学习(GLEAM)的模块演化与分析

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_10

A. Saini, L. Spector

引用次数: 0

Grammar-Based Vectorial Genetic Programming for Symbolic Regression 基于语法的向量遗传规划符号回归

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_2

Philipp Fleck, Stephan M. Winkler, M. Kommenda, M. Affenzeller

引用次数: 2

Feature Discovery with Deep Learning Algebra Networks 深度学习代数网络的特征发现

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-8113-4_6

Michael F. Korns

引用次数: 0