Survey on multiobjective evolutionary and real coded genetic algorithms
Mukesh M. Raghuwanshi, Omprakash G. Kakde, Rajiv Gandhi · 2004
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.