Abstract.tex

Most recombination operators are designed to provide an adaptive behavior with the principle of altering its exploration capabilities depending on the diversity of the population.
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However, depending only on the content of the population might be a drawback in long-term executions because diversity could not be large enough and the search process might prematurely converge.
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Based on the previous scenario, a novel recombination operator is designed for tackling continuous Multi-objective Optimization Problems (MOPs), which works effectively to enhance the search capability of Multi-Objective Evolutionary Algorithms (MOEAs).
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Particularly, this operator extends the Simulated Binary Crossover (SBX) by considering the stopping criterion to alter its internal operation.
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In order to validate the effectiveness of our proposal, it is studied by substituting the original recombination operators in  three state-of-the-art MOEAs: NSGA-II, MOEA/D and SMS-EMOA.
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The popular DTLZ, WFG and UF benchmark problems are taken into account.
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Experimental validation shows a significant improvement in the performance of all the MOEAs when applying the novel crossover operator.
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Additionally, our proposal is also tested against state-of-the-art differential evolution operators, providing quite competitive results.
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