Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The role of mutation and recombination in evolutionary algorithms
The role of mutation and recombination in evolutionary algorithms
Multicriteria Optimization
Computers and Operations Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP)
Computers and Operations Research
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In this paper, we first propose a new recombination operator called the two-stage recombination and then we test its performance in the context of the multiobjective 0/1 knapsack problem (MOKP). The proposed recombination operator generates only one offspring solution from a selected pair of parents according to the following two stages. In the first stage, called genetic shared-information stage or similarity-preserving stage, the generated offspring inherits all parent similar genes (i.e., genes or decision variables having the same positions and the same values in both parents). In the second stage, called problem fitness-information stage, the parent non-similar genes (i.e., genes or decision variables having the same positions but different values regarding the two parents) are selected from one of the two parents using some fitness information. Initially, we propose two different approaches for the second stage: the general version and the restricted version. However, the application of the restricted version to the MOKP leads to an improved version which is more specific to this problem. The general and the MOKP-specific versions of the two-stage recombination are compared against three traditional crossovers using two well-known multiobjective evolutionary algorithms. Promising results are obtained. We also provide a comparison between the general version and the MOKP-specific version.