In the NK model given by Kauffman, myopic local search involves flipping onerandomly-chosen bit of an N-bit decision string in every time step and accepting the new configuration if that has higher fitness . This algorithm consumes the full extent of computational resources allocated – given by the number of alternative configurations inspected – even though search is expected to terminate the moment there are no neighbors having higher fitness. Otherwise, the algorithm must compute the fitness of all N neighbors in every step . In order to getaround this problem, I describe an algorithm that allows search to logicallyterminate relatively early . I further suggest that when the efficacy of two algorithms need to be compared head to head, imposing a common limit on thenumber of alternatives evaluated

Author(s) : Sasanka Sekhar Chanda

Links : PDF - Abstract

Code :
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Keywords : search - algorithm - fitness - kauffman - myopic -

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