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Adaptive Reservoir Evolutionary Algorithm: An Evolutionary On-Line Adaptation Scheme for Global Function Optimization
Authors:C.?Munteanu  author-information"  >  author-information__contact u-icon-before"  >  mailto:cristi@omni.isr.ist.utl.pt"   title="  cristi@omni.isr.ist.utl.pt"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,A.C.?Rosa  author-information"  >  author-information__contact u-icon-before"  >  mailto:acrosa@laseeb.org"   title="  acrosa@laseeb.org"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author
Affiliation:(1) LaSEEB, Instituto de Sistemas e Robotica, Instituto Superior Tecnico, Av. Rovisco Pais 1, Torre Norte, 6.21, 1049-001, Lisboa, Portugal
Abstract:This paper introduces a novel global optimization heuristic algorithm based on the basic paradigms of Evolutionary Algorithms (EA). The algorithm greatly extends a previous strategy proposed by the authors in Munteanu and Lazarescu (1998). In the newly designed algorithm the exploration/exploitation of the search space is adapted on-line based on the current features of the landscape that is being searched. The on-line adaptation mechanism involves a decision process as to whether more exploitation or exploration is needed depending on the current progress of the algorithm and on the current estimated potential of discovering better solutions. The convergence with probability 1 in finite time and discrete space is analyzed, as well as an extensive comparison with other evolutionary optimization heuristics is performed on a set of test functions.
Keywords:Evolutionary Algorithms  fitness landscape  decision process  on-line adaptation  memetic-algorithms  metaheuristics  global optimization
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