{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T19:08:55Z","timestamp":1722452935510},"reference-count":47,"publisher":"MIT Press - Journals","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:p> Control parameter studies assist practitioners to select optimization algorithm parameter values that are appropriate for the problem at hand. Parameter values are well suited to a problem if they result in a search that is effective given that problem\u2019s objective function(s), constraints, and termination criteria. Given these considerations a many-objective tuning algorithm named MOTA is presented. MOTA is specialized for tuning a stochastic optimization algorithm according to multiple performance measures, each over a range of objective function evaluation budgets. MOTA\u2019s specialization consists of four aspects: (1) a tuning problem formulation that consists of both a speed objective and a speed decision variable; (2) a control parameter tuple assessment procedure that utilizes information from a single assessment run\u2019s history to gauge that tuple\u2019s performance at multiple evaluation budgets; (3) a preemptively terminating resampling strategy for handling the noise present when tuning stochastic algorithms; and (4) the use of bi-objective decomposition to assist in many-objective optimization. MOTA combines these aspects together with differential evolution operators to search for effective control parameter values. Numerical experiments consisting of tuning NSGA-II and MOEA\/D demonstrate that MOTA is effective at many-objective tuning. <\/jats:p>","DOI":"10.1162\/evco_a_00163","type":"journal-article","created":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T19:59:36Z","timestamp":1441137576000},"page":"113-141","source":"Crossref","is-referenced-by-count":3,"title":["MOTA: A Many-Objective Tuning Algorithm Specialized for Tuning under Multiple Objective Function Evaluation Budgets"],"prefix":"10.1162","volume":"25","author":[{"given":"Antoine S.","family":"Dymond","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aeronautical Engineering, University of Pretoria, South\u00a0Africa"}]},{"given":"Schalk","family":"Kok","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aeronautical Engineering, University of Pretoria, South\u00a0Africa"}]},{"given":"P. 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