Abstract
A key characteristic of Mixed-Integer (MI) problems is the presence of both continuous and discrete problem variables. These variables can interact in various ways, resulting in challenging optimization problems. In this paper, we study the design of an algorithm that combines the strengths of LTGA and iAMaLGaM: state-of-the-art model-building EAs designed for discrete and continuous search spaces, respectively. We examine and discuss issues which emerge when trying to integrate those two algorithms into the MI setting. Our considerations lead to a design of a new algorithm for solving MI problems, which we motivate and compare with alternative approaches.
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References
Bosman, P.A.N., Grahl, J., Thierens, D.: Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift. In: PPSN, pp. 133–143 (2008)
Bosman, P.A.N., Grahl, J., Thierens, D.: AMaLGaM IDEAs in noiseless black-box optimization benchmarking. In: GECCO (Companion), pp. 2247–2254 (2009)
Emmerich, M., Grötzner, M., Groß, B., Schütz, M.: Mixed-Integer Evolution Strategy for Chemical Plant Optimization with Simulators. In: Parmee, I.C. (ed.) Evolutionary Design and Manufacture, pp. 55–67. Springer, London (2000)
Emmerich, M.T.M., Li, R., Zhang, A., Flesch, I., Lucas, P.: Mixed-Integer Bayesian Optimization Utilizing A-Priori Knowledge on Parameter Dependences. In: BNAIC 2008, pp. 65–72 (2008)
Li, R., Emmerich, M.T.M., Eggermont, J., Bäck, T., Schütz, M., Dijkstra, J., Reiber, J.H.C.: Mixed Integer Evolution Strategies for Parameter Optimization. Evolutionary Computation 21(1), 29–64 (2013)
Runarsson, T., Yao, X.: Constrained evolutionary optimization. In: Evolutionary Optimization. International Series in Operations Research and Management Science, vol. 48, pp. 87–113. Springer, US (2002)
Thierens, D.: The linkage tree genetic algorithm. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 264–273. Springer, Heidelberg (2010)
Thierens, D., Bosman, P.A.N.: Optimal mixing evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 617–624. ACM, New York (2011)
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Sadowski, K.L., Thierens, D., Bosman, P.A.N. (2014). Combining Model-Based EAs for Mixed-Integer Problems. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_34
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DOI: https://doi.org/10.1007/978-3-319-10762-2_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
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