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    no debuggable processes_no port for remote debugger

     Always use the debug runtime during the development cycle  Use the release version during the development phase to measure the performance / CPU utilization of the application  Install an error handler via the mco_set_error_handler() API  If the execution is trapped inside the error handler, examine the application’s call stack and find the application’s function that issued the runtime call causing the assertion  Inspect mco.h file to see why the runtime asserted, e.g. an error in the transaction manager, heap corruption, a cursor is corrupted, etc. Check the appropriate application entity just before the fatal runtime call was issued and make sure that the entity –transaction handle, object handle or heap – is in fact corrupted  Go back through the stack and try to find the application code where the entity was corrupted.

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    不完全免疫算法简介AIMA--AIS学习笔记7

    Most multi-objective immune algorithms (MOIAs) adopt clonal selection to speed up convergence, as this operator only clones the best individuals during the search process. However, this approach somehow deteriorates the population diversity, which may cause a MOIA to be trapped in a local optimum and could also lead to premature convergence when tackling some complicated multiobjective optimization problems (MOPs). In order to overcome this problem, an adaptive immune-inspired multi-objective algorithm (AIMA) is presented in this paper, in which multiple differential evolution (DE) strategies having distinct advantages are embedded into a conventional MOIA. Our proposed approach strengthens the exploration capabilities of a MOIA while also improving its population diversity. At each generation, based on the current search stage, an adaptive selection method is designed to choose an appropriate DE strategy for evolution. The core idea is to effectively combine the advantages of three DE strategies when solving different MOPs. A number of comparative experiments are conducted on the well-known and frequently- used WFG and DTLZ test problems. Our experimental results validate the superiority of our proposed AIMA, as it performs better than some state-of-the-art multi-objective opti- mization algorithms and some state-of-the-art MOIAs.

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