Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Fitness Inheritance In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
Noisy Multiobjective Optimization on a Budget of 250 Evaluations
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Digital Ecosystems: Ecosystem-Oriented Architectures
Natural Computing: an international journal
Multiobjective optimization on a budget of 250 evaluations
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
International Journal of Wireless and Mobile Computing
Hi-index | 0.00 |
Fitness evaluation in real-world applications often causes a lot of computational overhead. Fitness inheritance has been proposed for tackling this problem. Instead of evaluating each individual, a certain percentage of the individuals is evaluated indirectly by interpolating the fitness of their parents. However, the problems on which fitness inheritance has been tested are very simple and the question arises whether fitness inheritance is really useful for real-world applications. The objective of this paper is to test the performance of average and weighted average fitness inheritance on a well-known test suite of multiple objective optimization problems. These problems have been generated as to constitute a collection of test cases for genetic algorithms. Results show that fitness inheritance can only be applied to convex and continuous problems.