Abstract
Path route layout design is very actual and complex problem, important for improvement of transportation, traffic, travel or roads planning. Solution domain is huge and finding satisfying solution is not an easy task. Increase of number of route path points and/or increase of their resolution significantly influences search-space. This paper is focused on use of genetic algorithm for effective narrowing the search-space and focusing on interesting regions. The importance of its main parameters, their impact on the performance and the precision of the genetic algorithm are elaborated in this paper. Three domain specific solution classes based on step-by-step improvements have been presented. In order to gain high efficiency and effectiveness, guidelines for a proper set-up of genetic algorithm are given. Encoding of parameters, build-up of the fitness function, evaluation and reproduction of chromosomes, elitism and convergence affinity are discussed in detail. Advanced built-in mechanisms have been emphasized with discussion of performance improvement.
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References
Avdagic Z (2003a) Artificial intelligence & expert systems. Grafoart, Sarajevo
Avdagic Z (2003b) Artificial intelligence & fuzzy-neuro-genetic. Grafoart, Sarajevo
Avdagic Z, Konjicija S, Dervisbegovic F (2001) Genetic algorithms in optimization of engineering problems. In: Proceedings of ICT 2001. ETF, Sarajevo
Avdagic Z, Smajevic A, Boskovic D (2008) Track layout design using modified genetic algorithm. In: Proceedings of IEEE international symposium on industrial electronics, Cambridge
Bigus JP, Bigus J (2001) Constructing intelligent agents using Java. Wiley, New York
Bera R, Claramunt C (2004) Can the relative adjacency contribute to space syntax in the search for a structural logic of the city. In: Proceedings of international GI science conference, Washington
Cantarella GE, Vitetta A (2006) The multi-criteria road network design problem in an urban area. Transportation 33:567–588
Carvalho D, Maia T (2011) The simple genetic algorithm performance: a comparative study on the operators combination. In: Proceedings of the first international conference on advanced communications and computation (INFOCOMP)
Chen RC, Chen CT, Li JY (2012) A genetic algorithm for planning travel route with minimum transportation carbon footprint. In: Qu X, Yang Y (eds) Information and business intelligence, IBI 2011, Communications in computer and information science, vol 268. Springer, Heidelberg
Eiben AE, Schut MC, Wild AR (2006) Boosting genetic algorithms with self-adaptive selection. In: Proceedings of IEEE congress on evolutionary computation, Vancouver, pp 477–482
Gao M, Shi G, Li W, Wang Y, Liu D (2017) An improved genetic algorithm for island route planning. Proc Eng 174:433–441
Han Z, Wang D, Liu F, Zhao Z (2017) Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PLoS One 12(7):e0181747
Hillier B (1996) Space is the machine. Cambridge University Press, Cambridge
Koma S, Yamabe Y, Tani A (2017) Research on urban landscape design using the interactive genetic algorithm and 3D images. Vis Eng 5(1):1
Korejo I, Yang S, Changhe L (2009) A comparative study of adaptive mutation operators for genetic algorithms. In: Proceedings of the VIII metaheuristics international conference, Hamburg
Mitchell M (1998) An introduction to genetic algorithms. The MIT Press, Cambridge
Nagar A, Tawfik H (2007) A multi-criteria approach in prototyping urban road networks. In: Proceedings of informing science + information technology education joint conference, Ljubljana
Nanayakkara S, Srinivasan D, Lai LW, German X, Taylor E, Ong S (2007) Genetic algorithm based route planner for large urban street networks. In: Proceedings of IEEE congress on evolutionary computation, pp 4469–4474
Ortega P, Jose L (2001) Reference analysis base on a vectorial spaces model: contemporary history in Jaen research for 1990–1995. Revista Espaola de Documentacin Cientfica 24
Peponis J, Zimring C, Choi YK (1990) Finding the building in wayfinding. Environ Behav 33:555–590
Turcinhodzic R, Avdagic Z, Omanovic S (2013) Modified GA with the possibility of selecting a selection operator according to a set criterion. Int J Comput 7(4):153–161
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):670–82 IEEE
Yang S (2002) Adaptive non-uniform crossover based on statistics for genetic algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO), pp 650–657
Yang S (2003) Statistics-based adaptive non-uniform mutation for genetic algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO) part II, Berlin, pp 1618–1619
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Avdagic, Z., Smajevic, A., Omanovic, S. et al. Path route layout design optimization using genetic algorithm: based on control mechanisms for on-line crossover intersection positions and bit targeted mutation. J Ambient Intell Human Comput 13, 835–847 (2022). https://doi.org/10.1007/s12652-021-02937-z
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DOI: https://doi.org/10.1007/s12652-021-02937-z