A Probe Guided Crossover Operator for More Efficient Exploration of the Search Space
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Crossover operators play very important role for the entire performance of evolutionary algorithms, as through the recombination of the fittest solutions, guide the population towards higher fitness regions of the search space. This study proposes a new Probe Guided Crossover (PGC) operator for the more efficient exploration of the search space. The proposed recombination operator is applied to three well-known Multiobjective Evolutionary Algorithms (MOEAs) namely the Non-dominated Sorting Genetic Algorithm II (NSGAII), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Indicator Based Evolutionary Algorithm (IBEA) and its performance is evaluated in comparison with the Simulated Binary Crossover (SBX). The proposed methodology is tested with the assistance of five test instances from the Li-Zhang (LZ) set of test functions. The experiments show that the PGC operator generates better results with confidence than the SBX operator.
KeywordsMultiobjective optimization Evolutionary algorithms Crossover
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