{"paper":{"title":"General-Purpose Co-Evolutionary Construction of Parallel Algorithm Portfolios for Multi-Objective Binary Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A co-evolutionary method builds parallel algorithm portfolios that apply directly to multiple multi-objective binary optimization problems without custom generators.","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Ke Tang, Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang","submitted_at":"2026-05-15T08:32:47Z","abstract_excerpt":"Despite recent progress in constructing generalizable parallel algorithm portfolios (PAPs), no general-purpose approach is yet available for multi-objective binary optimization problems (MOBOPs). To fill this gap, this paper proposes domain-agnostic co-evolution of parameterized search for multi-objective binary optimization~(DACMO), which features two technical innovations. First, we propose a neural instance representation architecture that decouples domain-invariant and instance-specific features, enabling class-consistent instance generation across varying dimensions without problem-specif"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"DACMO can be directly applied to all four problem classes without modification, outperforms PAPs built from classic MOEA templates, and achieves performance comparable to a privileged state-of-the-art baseline that relies on manually designed problem-specific instance generators, while outperforming it on two of the four evaluated problem classes.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The proposed neural instance representation architecture successfully decouples domain-invariant and instance-specific features, enabling class-consistent instance generation across varying dimensions without requiring problem-specific instance generators.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DACMO constructs general-purpose parallel algorithm portfolios for multi-objective binary optimization via co-evolution of neural instance representations and LLM-generated operators, performing competitively on four problem classes without problem-specific generators.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A co-evolutionary method builds parallel algorithm portfolios that apply directly to multiple multi-objective binary optimization problems without custom generators.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ec5d41dc9e6e02bce52006c4220e59f8e8a26bc17d830f66ad7723b9b113e0c1"},"source":{"id":"2605.15729","kind":"arxiv","version":1},"verdict":{"id":"b39d7456-882b-41ee-8d21-209458cca8ed","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:37:49.406018Z","strongest_claim":"DACMO can be directly applied to all four problem classes without modification, outperforms PAPs built from classic MOEA templates, and achieves performance comparable to a privileged state-of-the-art baseline that relies on manually designed problem-specific instance generators, while outperforming it on two of the four evaluated problem classes.","one_line_summary":"DACMO constructs general-purpose parallel algorithm portfolios for multi-objective binary optimization via co-evolution of neural instance representations and LLM-generated operators, performing competitively on four problem classes without problem-specific generators.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The proposed neural instance representation architecture successfully decouples domain-invariant and instance-specific features, enabling class-consistent instance generation across varying dimensions without requiring problem-specific instance generators.","pith_extraction_headline":"A co-evolutionary method builds parallel algorithm portfolios that apply directly to multiple multi-objective binary optimization problems without custom generators."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15729/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.206905Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:51:29.045724Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:25.148061Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.995338Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"7f4e3d200c2f521c2218411e9735ce6442737b9e7f09a3c811819d79493af7aa"},"references":{"count":50,"sample":[{"doi":"","year":2007,"title":"C. 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