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Adaptive Mutation Rate at Genetic Algorithms with Multi-Chromosome Representation in Multi-department Hospital Process Optimization

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Matthias Kühn, Thomas Severin, Joachim Lippold, Horst Salzwedel, Volker Nissen

Matthias Kühn, Thomas Severin, Joachim Lippold, Horst Salzwedel and Volker Nissen. Adaptive Mutation Rate at Genetic Algorithms with Multi-Chromosome Representation in Multi-department Hospital Process Optimization. International Journal of Computer Applications 182(21):41-52, October 2018. BibTeX

	author = {Matthias Kühn and Thomas Severin and Joachim Lippold and Horst Salzwedel and Volker Nissen},
	title = {Adaptive Mutation Rate at Genetic Algorithms with Multi-Chromosome Representation in Multi-department Hospital Process Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2018},
	volume = {182},
	number = {21},
	month = {Oct},
	year = {2018},
	issn = {0975-8887},
	pages = {41-52},
	numpages = {12},
	url = {},
	doi = {10.5120/ijca2018917980},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The performance of Metaheuristics in general and Evolutionary Algorithms (EA) in particular depends on good settings of algorithm parameter values, such as population size, mutation rate or crossover probability. To increase performance, researchers still try to find optimal settings. At present, researchers are adapting the parameter settings during an evolutionary run (parameter control). Thus, no hand tuning is needed upfront of an evolutionary run. In this paper we analyze algorithm performance when using adaptable algorithm parameters on Genetic Algorithms (GA) with multi-chromosome representation. Most of the research in the field of EA has been done on a theoretical basis. Often the proposed solutions do not deliver what they promise, when applying them to complex problems of real-world. Thus, experimental studies on complex problems of real-world are needed to ascertain performance improvement of adaptive parameter control. This paper is an experimental study on such a complex optimization problem of real-world (dynamically coupled System of Systems). In our approach of parameter control new individuals are generated by adapting the mutation rate. Therefore, we calculate a dedicated mutation rate for each chromosome of the individual. This happens in relation to the fitness of each chromosome. We analyzed and have statistically proven the outperformance of our approach upfront with the De Jong’s (Sphere) and the Schwefel’s test function. In this paper, we are now applying our approach to a real world based complex optimization problem (nonstationary, dynamic, noisy), to prove the outperformance of our approach. Therefore, we made a performance comparison with non-adaptive GA, which demonstrates the superiority of the adaptive approach. More specifically, we use a stochastic simulation model of university hospital processes. Inpatient admission, outpatient admission and op-theater planning of elective patients must be optimized simultaneously, while emergencies occur. Every hospital area has its own objectives and constraints (dedicated systems). The number of patients and utilization of resources must be maximized in every hospital area, while waiting times, lead times and schedule variances must be minimized. In that, a system of systems can be seen. It is shown how our approach can be used to optimize such dynamically coupled system of systems (SoS) in an efficient way.


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Genetic algorithms, hospital, inpatient admission, multi-chromosome, mutation rate, op-theater planning. optimization, outpatient admission, parameter control, self-adapting, computer simulation, real world problem, system of systems optimization.