Survey on multiobjective evolutionary and real coded genetic algorithms
Survey on multiobjective evolutionary and real coded genetic algorithms
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发表时间：
2004
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通讯作者：
M. Raghuwanshi;O. Kakde;R. Gandhi
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作者：
M. Raghuwanshi;O. Kakde;R. Gandhi
进化算法(EA)具有解决现实世界中满足要求的优化问题的几个特征。多目标进化算法(MOEA)是针对两个共同目标而设计的，即快速可靠地收敛到Pareto集和解沿前沿的良好分布。实际上，每种算法都代表了实现这些目标的特定技术的独特组合。用二进制编码遗传算法处理连续搜索空间有几个困难。实数编码遗传算法表示参数不需要编码，这使得解的表示非常接近于许多问题的自然公式。在实数编码遗传算法(RCGA)中，重组和变异算子被设计成与实数参数一起工作。本综述介绍了多目标进化算法和实数编码遗传算法的研究现状。
Evolutionary Algorithm (EA) possesses several characteristics that are desirable to solve real-world optimization problems up to a required level of satisfaction. Multiobjective Evolutionary Algorithms (MOEAs) are designed with regard to two common goals, fast and reliable convergence to the Pareto set and a good distribution of solutions along the front. Virtually each algorithm represents a unique combination of specific techniques to achieve these goals. Handling continuous search space with binary coded genetic algorithm has several difficulties. Real coded genetic algorithm represents parameters without coding, which makes representation of the solutions very close to the natural formulation of many problems. In real coded GA (RCGA) recombination and mutation operators are designed to work with real parameters. This survey gives state-of‐the-art of multiobjective evolutionary algorithms and real coded genetic algorithms.