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EvolutionaryMultimodalOptimization
+requires+finding+all+optimal+solutions%252C+which+is+challenging+in+limited+function+evaluations.+Although+existing+works+strike+the+balance+of+exploration+and+exploitation+through+hand-crafted+adaptive+strategies%252C+they+require+certain+expert+knowledge%252C+hence+inflexible+to+deal+with+MMOP+with+different+properties.+In+this+paper%252C+we+propose+RLEMMO%252C+a+Meta-Black-Box+Optimization+framework%252C+which+maintains+a+population+of+solutions+and+incorporates+a+reinforcement+learning+agent+for+flexibly+adjusting+individual-level+searching+strategies+to+match+the+up-to-date+optimization+status%252C+hence+boosting+the+search+performance+on+MMOP.+Concretely%252C+we+encode+landscape+properties+and+evolution+path+information+into+each+individual+and+then+leverage+attention+networks+to+advance+population+information+sharing.+With+a+novel+reward+mechanism+that+encourages+both+quality+and+diversity%252C+RLEMMO+can+be+effectively+trained+using+a+policy+gradient+algorithm.+The+experimental+results+on+the+CEC2013+MMOP+benchmark+underscore+the+competitive+optimization+performance+of+RLEMMO+against+several+strong+baselines.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.c70dd34a8fb0409a02678294432b4baa%3frik%3digSqzKeo16pWyw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Evolutionary+Multimodal+Optimization&FORM=IRPRST&ck=6968368D1FCB37E060CE1977B8B0680A&selectedIndex=17&itb=0)
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