Abstract
The maximin fitness function can be used in multi-objective genetic algorithms to obtain a diverse set of non-dominated designs. The maximin fitness function is derived from the definition of dominance, and its properties are explored. The modified maximin fitness function is proposed. Both fitness functions are briefly compared to a state-of-the-art fitness function from the literature. Results from a real-world multi-objective problem are presented. This problem addresses land-use and transportation planning for high-growth cities and metropolitan regions.
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References
Balling, R. J., Pareto sets in decision-based design. Journal of Engineering Valuation and Cost Analysis, 3:189–198, (2000)
Goldberg, D.E., Genetic Algorithms for Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, USA (1989)
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T., A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Proceedings of Parallel Problem Solving from Nature VI Conference (PPSN-VI):849–858 (2000)
Schaffer, J.D., Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms, Ph.D. Dissertation, Vanderbilt University, Nashville, TN, USA (1984)
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© 2003 Springer-Verlag Berlin Heidelberg
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Balling, R. (2003). The Maximin Fitness Function; Multi-objective City and Regional Planning. 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_1
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DOI: https://doi.org/10.1007/3-540-36970-8_1
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Publisher Name: Springer, Berlin, Heidelberg
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