An experimental study of adaptive control for evolutionary algorithms |
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| Affiliation: | 1. Dipartimento di Management, Universitá Ca Foscari, Venezia, Italy;2. LERIA, University of Angers, France;3. Instituto de Informática, Universidad Austral de Chile, Chile;1. School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500 Changshu, China;2. School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, China;1. Donostia International Physics Center (DIPC), Paseo Manuel de Lardizabal 4, E-20018 San Sebastián, Spain;2. Centro de Física de Materiales CFM-MPC, Centro Mixto CSIC-UPV/EHU, Paseo Manuel de Lardizabal 5, E-20018 San Sebastián, Spain;3. LOMA, Université de Bordeaux 1, 351 Cours de la Liberation, 33405 Talence, France;1. Centro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia 58089, Michoacán, Mexico;2. Steklov Mathematical Institute, 8 Gubkin Street, Moscow 119991, Russia;1. Pondicherry University (A Central University of India), India;2. Periyar Govt. College, Cuddalore, India;3. National University of Kaohsiung, Taiwan |
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| Abstract: | In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm. |
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| Keywords: | Algorithms Design experimentation Measurement Performance Evolutionary algorithms Adaptive operator selection |
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