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The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solutions (parents), where new solutions are expected to be better than existing solutions. This paper presents a new parent selection method for the crossover operation in genetic programming. The idea is to promote crossover between two behaviourallydoi:10.1016/j.asoc.2018.03.035 fatcat:ttitkmmmcbffhdq3v4psd3hzam