Abstract
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.
Keywords
- Genetic Algorithm
- Multiobjective Optimization
- Multiobjective Evolutionary Algorithm
- Standard Genetic Algorithm
- Multiple Objective Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ducheyne, E., De Baets, B., De Wulf, R. (2003). Is Fitness Inheritance Useful for Real-World Applications?. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_3
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DOI: https://doi.org/10.1007/3-540-36970-8_3
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