Genetic algorithm for multi-objective experimental optimization

Bioprocess and Biosystems Engineering

Volumn 29, Issue 5-6, 2006, Pages 385-390

  Link, Hannes ^a   Weuster Botz, Dirk ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Experimental design; Genetic algorithm; Multi objective optimization; Software tool

Indexed keywords

COMPUTER SOFTWARE; PARAMETER ESTIMATION; PARETO PRINCIPLE; STRUCTURAL DESIGN;

EXPERIMENTAL DESIGN; MULTI OBJECTIVE OPTIMIZATION; PARETO EVOLUTIONARY ALGORITHM; SOFTWARE TOOL;

GENETIC ALGORITHMS;

ARTICLE; BIOPROCESS; COMPUTER PROGRAM; EVALUATION; EXPERIMENTAL DESIGN; GENETIC ALGORITHM; MULTI OBJECTIVE EXPERIMENTAL OPTIMIZATION; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

ALGORITHMS; COMPUTER SIMULATION; MODELS, BIOLOGICAL; QUALITY CONTROL; SOFTWARE; SYSTEMS THEORY;

EID: 33751265229     PISSN: 16157591     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00449-006-0087-7     Document Type: Article

References (12)

1. Weuster-Botz D (2000) Experimental design for fermentation media development: statistical design or global random search? J Biosci Bioeng 90:473-483
2. Rechenberg I (1973) Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Friedrich Frommann Verlag, Stuttgart
3. Holland HJ (1975) Adaption in natural and artificial systems, an introductory analysis with application to biology, control and artificial intelligence. The University of Michigan Press, Ann Arbor
4. Schaffer JD (1985) Multi-objective optimization with vector evaluated genetic algorithms. In: Proceedings of an international conference on genetic algorithms and their applications, pp 93-100
5. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the Parallel Problem Solving from Nature, vol VI, pp 849-858
6. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3:257-271
7. Chakraborty M, Chakraborty UK (1997) An analysis of linear ranking and binary tournament selection in genetic algorithms. Proc Int Conf Inf Commun Signal Process 1:407-411
8. Kim JW, Kim SW, Park P, Park TJ (2002) On the similarities between binary-coded GA and real-coded GA in wide search space. In: Proceedings of the 2002 Congress on evolutionary computation, vol 1, pp 681-686
9. Reeves CR (1993) Using genetic algorithms with small populations. In: Forrest S (ed) Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann Publisher, San Mateo, pp 92-99
10. Havel J, Link H, Hofinger M, Franco-Lara E, Weuster-Botz D (2006) Comparison of genetic algorithms for experimental multi-objective optimization on the example of medium design for cyanobacteria. Biotechnol J 1:549-555
11. Deb K, Thiele L, Laumanns M, Zitzler E (2002) Scalable multi objective optimization test problems. In: Proceedings of the 2002 Congress on evolutionary computation, vol 1, pp 825-830
12. Weuster-Botz D, Puskeiler R, Kusterer A, Kaufmann K, John GT, Arnold M (2005) Methods and milliliter scale devices for high-throughput bioprocess design. Bioprocess Biosyst Eng 28:109-119