Thanh-Do Tran

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The idea of multiobjectivization is to reformulate a single-objective problem as a multiobjective one. In one of the scarce studies proposing this idea for problems in <i>continuous</i> domains, the distance to the closest neighbor (DCN) in the population of a multiobjective algorithm has been used as the additional (dynamic) second objective. As no(More)
Algorithm benchmarking plays a vital role in designing new optimization algorithms and in recommending efficient and robust algorithms for practical purposes. So far, two main approaches have been used to compare algorithms in the evolutionary multiobjective optimization (EMO) field: (i) displaying empirical attainment functions and (ii) reporting(More)
Genetic algorithms--a class of stochastic population-based optimization techniques--have been widely realized as the effective tools to solve complicated optimization problems arising from the diverse application domains. Originally developed version was a genetic algorithm with the binary representation of candidate solutions (i.e. chromosomes), the(More)
Originally, genetic algorithms were developed based on the binary representation of candidate solutions in which each conjectured solution is a fixed-length string of binary numbers; however, real-valued representation scheme is basically superior and frequently utilized in addressing hard optimization tasks, particularly for the optimization in continuous(More)
Due to stochastic nature, the success of evolutionary methodologies in search for solutions to optimization problems depends greatly on the harmonization between exploration and exploitation. However, the difficulties in compromising on these two properties properly make the design of an efficient evolutionary algorithm (EA) become one of the laborious(More)
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