Summary
This book explores some emerging techniques for problem solving of a general nature, based on the tools of EMO. In this introduction, we provide background material to support the reader’s journey through the succeeding chapters. Given here are a basic introduction to optimization problems, and an introductory treatment of evolutionary computation, with thoughts on why this method is so successful; we then discuss multiobjective problems, providing definitions that some future chapters rely on, covering some of the key concepts behind multiobjective optimization. These show how optimization can be carried out separately from subjective factors, even when there are multiple and conflicting ends to the optimization process. This leads to a set of trade-off solutions none of which is inherently better than any other. Both the process of multiobjective optimization, and the set of trade-offs resulting from it, are ripe areas for innovation — for new techniques for problem solving. We briefly preview how the chapters of this book exploit these concepts, and indicate the connections between them.
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References
N. A. Barricelli. Numerical testing of evolution theories Part II: Preliminary tests of performance, symbiogenesis and terrestrial life. Acta Biotheoretica, 16 (3–4): 99–126, 1963.
V. Belton and T. J. Stewart.Multiple Criteria Decision Analysis: an Integrated Approach. Springer-Verlag, Berlin, Germany, 2002.
H. J. Bremermann. Optimization through evolution and recombination. In Self-Organizing Systems, pages 93–106. Spartan Books, Washington, DC, 1962.
A. Chandra and X. Yao. Ensemble Learning Using Multi-Objective Evolutionary Algorithms. Journal of Mathematical Modelling and Algorithms, 5(4):417– 445, 2006.
C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.
D. Corne, K. Deb, P. Fleming, and J. Knowles. The Good of the Many Outweighs the Good of the One: Evolutionary Multi-Objective Optimization. IEEE Connections Newsletter, 1(1):9–13, 2003.
K. Deb. Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design. In K. Miettinen, M. M. Mäkelä, P. Neittaanmäki, and J. Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, chapter 8, pages 135–161. John Wiley & Sons, Ltd, Chichester, UK, 1999.
J. S. Dyer, P. C. Fishburn, R. E. Steuer, J. Wallenius, and S. Zionts. Multiple criteria decision making, multiattribute utility theory: the next ten years. Management Science, 38(5):645–654, 1992.
G. Dyson. Darwin Among the Machines. Penguin Books, 1999.
M. Ehrgott. Multicriteria Optimization. Number 491 in Lecture Notes in Economics and Mathematical Systems. Springer, Berlin, 2000.
M. Ehrgott and X. Gandibleux. An Annotated Bibliography of Multi-objective Combinatorial Optimization. Technical Report 62/2000, Fachbereich Mathematik,Universit¨at Kaiserslautern, Kaiserslautern, Germany, 2000.
D. Fogel. Asymptotic convergence properties of genetic algorithms and evolutionary programming: Analysis and experiments. Cybernetics and Systems,25(3):389–407, 1994.
D. B. Fogel. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 5(1):3–14, 1994.
L. J. Fogel. Autonomous automata. Industrial Research, 4(1):14–19, 1962.
C. M. Fonseca and P. J. Fleming. An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation, 3(1):1–16, Spring 1995.
A. S. Fraser. Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Sciences, 10:484–491, 1957.
M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP Completeness. W. H. Freeman and Company, San Francisco, 1979.
A. M. Geoffrion, J. S. Dyer, and A. Feinberg. An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department. Management Science, 19(4):357–368, 1972.
J. H. Holland. Adaption in Natural and Artificial Systems. University of Michigan Press, 1975.
R. L. Keeney and H. Raiffa. Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Cambridge University Press, Cambridge, UK, 1993.
J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer, 2002.
J. Lohn, G. Hornby, and D. Linden. An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission. In Genetic Programming Theory and Practice II, pages 13–15. Springer, 2004.
A. V. Lotov, V. A. Bushenkov, and G. K. Kamenev. Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Kluwer Academic, 2004.
K. Miettinen. Nonlinear Multiobjective Optimization. Kluwer Academic Publishers,1999.
R. Poli and W. Langdon. Schema Theory for Genetic Programming with One-Point Crossover and Point Mutation. Evolutionary Computation, 6(3):231–252,1998.
G. R. Price. Selection and covariance. Nature, 227:520–521, 1970.
A. Pryke, S. Mostaghim, and A. Nazemi. Heatmap visualization of population based multi objective algorithms. In Evolutionary Multi-Criterion Optimization (EMO 2006), volume 4403 of LNCS, pages 361–375. Springer, 2006.
N. Radcliffe. The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence, 10(4):339–384, 1994.
I. Rechenberg. Cybernetic solution path of an experimental problem, 1965. Library Translation 1122, Royal Aircraft Establishment, Farnborough, UK.
G. Rudolph. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1):96–101, 1994.
G. Rudolph. Convergence of evolutionary algorithms in general search spaces.In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 50–54, 1996.
H.-P. Schwefel. Kybernetische Evolution als Strategie der experimentellen Forschung in der Stromungstechnik. Master’s thesis, Hermann F¨ottinger Institute for Hydrodynamics, Technical University of Berlin, 1965.
Y. Siskos and A. Spyridakos. Intelligent multicriteria decision support: Overview and perspectives. European Journal of Operational Research, 113(2):236–246, 1999..
R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. Mehra. Discovering Novel Fighter Combat Maneuvers in Simulation: Simulating Test Pilot Creativity. In Creative Evolutionary Systems, pages 467–486. Morgan Kaufmann, 2001.
N. Srinivas and K. Deb. Multiobjective optimization using nondominated sorting in genetic algorithms. Technical report, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India, 1993.
D. Thierens and D. Goldberg. Convergence models of genetic algorithm selectionschemes. In Parallel Problem Solving from Nature — PPSN III, volume 866 of LNCS, pages 119–129, 1994.
P. Vincke. Multicriteria Decision-aid. Wiley, New York, 1992.
D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1:67–82, 1997.
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Knowles, J., Corne, D., Deb, K. (2008). Introduction: Problem Solving, EC and EMO. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_1
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