{"work":{"id":"14b4547d-c2f5-4df0-88d7-c7f931a9cb2d","openalex_id":null,"doi":null,"arxiv_id":"1604.00772","raw_key":null,"title":"The CMA Evolution Strategy: A Tutorial","authors":null,"authors_text":"Nikolaus Hansen (TAO)","year":2016,"venue":"cs.LG","abstract":"This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.","external_url":"https://arxiv.org/abs/1604.00772","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-06-29T07:53:13.293098+00:00","pith_arxiv_id":"1604.00772","created_at":"2026-05-09T22:44:14.676461+00:00","updated_at":"2026-06-29T07:53:13.293098+00:00","title_quality_ok":false,"display_title":"The CMA Evolution Strategy: A Tutorial","render_title":"The CMA Evolution Strategy: A Tutorial"},"hub":{"state":{"work_id":"14b4547d-c2f5-4df0-88d7-c7f931a9cb2d","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":48,"external_cited_by_count":null,"distinct_field_count":19,"first_pith_cited_at":"2019-06-21T08:56:29+00:00","last_pith_cited_at":"2026-06-10T17:59:34+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-06-29T09:08:33.100421+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":4},{"context_role":"method","n":2}],"polarity_counts":[{"context_polarity":"background","n":3},{"context_polarity":"use_method","n":2},{"context_polarity":"unclear","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}