Fitness Inheritance in Multi-Objective Optimization


In real-world multi-objective problems, the evaluation of objective functions usually requires a large amount of computation time. Moreover, due to the curse of dimensionality, solving multi- objective problems often requires much longer computation time than solving single-objective problems. Therefore, it is essential to develop ef- ficiency enhancement t echniques for solving multi-objective problems. This paper investi- gates fitness inheritance as a way to speed up multi-objective genetic and evolutionary algo- rithms. Convergence and population-sizing mod- els are derived and compared with experimental results in two cases: fitness inheritance without fitness sharing and fitnes s inheritance with fitness sharing. Results show that the number of function evaluations can be reduced with the use of fitness inheritance.