--- abstract: "In this paper, a new method for assigning credit to search\r\noperators is presented. Starting with the principle of optimizing\r\nsearch bias, search operators are selected based on an ability to\r\ncreate solutions that are historically linked to future generations.\r\nUsing a novel framework for defining performance\r\nmeasurements, distributing credit for performance, and the\r\nstatistical interpretation of this credit, a new adaptive method is\r\ndeveloped and shown to outperform a variety of adaptive and\r\nnon-adaptive competitors." altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: 'July 8-12, 2006' conference: GECCO 2006 confloc: 'Seattle, Washington, USA' contact_email: ~ creators_id: [] creators_name: - family: Whitacre given: James M honourific: Dr lineage: '' - family: Pham given: Tuan Q honourific: Dr lineage: '' - family: Sarker given: Ruhul A honourific: Dr lineage: '' date: 2006-07-08 date_type: published datestamp: 2009-07-06 09:42:42 department: ~ dir: disk0/00/00/65/80 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 6580 fileinfo: /style/images/fileicons/application_pdf.png;/6580/1/Credit_Assignment_in_adaptive_evolutionary_algorithms%2Dwhitacre.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: "Evolutionary Algorithm, Genetic Algorithm, Adaptation,\r\nHistorical Credit Assignment, Search Bias" lastmod: 2011-03-11 08:57:23 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: TRUE publication: ~ publisher: ~ refereed: TRUE referencetext: "[1] Barbosa, H. J. C. and e Sá, A. M. On Adaptive Operator\r\nProbabilities in Real Coded Genetic Algorithms, In\r\nWorkshop on Advances and Trends in Artificial Intelligence\r\nfor Problem Solving (SCCC '00), (Santiago, Chile,\r\nNovember 2000).\r\n[2] Davis, L. Handbook of Genetic Algorithms, van Nostrand\r\nReinhold, New York, 1991.\r\n[3] De Jong, K. An analysis of the behaviour of a class of\r\ngenetic adaptive systems. Ph. D Thesis, University of\r\nMichigan, Ann Arbor, Michigan, 1975.\r\n[4] Herrera, F. and Lozano, M. Tackling real-coded genetic\r\nalgorithms: Operators and tools for the behavioural analysis,\r\nArtificial Intelligence Review 12, 4, (1998), 265-319.\r\n[5] Herrera, F., Lozano, M., and Sánchez, A. M. 2005. Hybrid\r\ncrossover operators for real-coded genetic algorithms: an\r\nexperimental study. Soft Comput. 9, 4 (Apr. 2005), 280-298.\r\n[6] Janka, E. Vergleich stochastischer Verfahren zur globalen\r\nOptimierung, Diploma Thesis, University of Vienna, Vienna,\r\nAustria, 1999.\r\n[7] Julstrom, B. A. Adaptive operator probabilities in a genetic\r\nalgorithm that applies three operators. In Proceedings of the\r\n1997 ACM Symposium on Applied Computing (SAC '97)\r\n(San Jose, California, United States). ACM Press, New\r\nYork, NY, 233-238, 1997.\r\n[8] Muhlenbein, H., Schomisch, M. and Born, J. The parallel\r\ngenetic algorithm as function optimizer. In Proc. of 4th\r\nInternational Conference of Genetic Algorithms, 271-278,\r\n1991.\r\n[9] Pham, Q.T. Dynamic Optimization of Chemical Engineering\r\nProcesses by an Evolutionary Method. Comp. Chem. Eng.,\r\n22 (1998), 1089-1097.\r\n[10] Pham, Q. T. Competitive evolution: a natural approach to\r\noperator selection. In: Progress in Evolutionary\r\nComputation, Lecture Notes in Artificial Intelligence,\r\n(Evolutionary Computation Workshop) (Armidale, Australia,\r\nNovember 21-22, 1994). Springer-Verlag, Heidelberg, 1995,\r\n49-60.\r\n[11] Storn, R. and Price, K. Differential Evolution - A Simple and\r\nEfficient Adaptive Scheme for Global Optimization over\r\nContinuous Spaces. Technical Report TR-95-012,\r\nInternational Computer Science Institute, Berkeley, CA,\r\n1995.\r\n[12] Whitacre, J., Pham, Q.T., Sarker, R. Use of Statistical\r\nOutlier Detection Method in Adaptive Evolutionary\r\nAlgorithms. In Proceedings of the 2006 Conference on\r\nGenetic and Evolutionary Computation (GECCO '05)\r\n(Seattle, USA, July 8-12, 2006). ACM Press, New York, NY,\r\n2006." relation_type: [] relation_uri: [] reportno: ~ rev_number: 20 series: ~ source: ~ status_changed: 2009-07-06 09:42:42 subjects: - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Credit Assignment in Adaptive Evolutionary Algorithms type: confpaper userid: 8971 volume: ~