Abstract
Seldom is it practical to completely automate the discovery of the Pareto Frontier by genetic programming (GP). It is not only difficult to identify all of the optimization parameters a-priori but it is hard to construct functions that properly evaluate parameters. For instance, the “ease of manufacture” of a particular antenna can be determined but coming up with a function to judge this on all manner of GP-discovered antenna designs is impractical. This suggests using GP to discover many diverse solutions at a particular point in the space of requirements that are quantifiable, only a-posteriori (after the run) to manually test how each solution fares over the less tangible requirements e.g.“ease of manufacture”. Multiple solutions can also suggest requirements that are missing. A new toy problem involving collision avoidance is introduced to research how GP may discover a diverse set of multiple solutions to a single problem. It illustrates how emergent concepts (linguistic labels) rather than distance measures can cluster the GP generated multiple solutions for their meaningful separation and evaluation.
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References
Penrose, R.: A generalized inverse for matrices. Proceedings of the Cambridge Philosophical Society 51, 406–413 (1955)
Solms, M.: Freud Returns. Scientific American, April 26 (2004)
Kurzweil, R.: The Singularity is Near: when humans transcend biology. Viking Press, New York (2005)
Deb, K., Gupta, H.: Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 150–164. Springer, Heidelberg (2005)
Johnson, R.C.: Antenna Engineering Handbook, 3rd edn. McGraw-Hill, New York (1993)
Burke, G.J., Miller, E.K., Poggio, A.J.: The Numerical Electromagnetics Code (NEC) - a brief history. In: IEEE Antennas and Propagation Society International Symposium, vol. 3, pp. 2871–2874 (2004)
Hu, J., Goodman, E.D., Seo, K.: Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming. In: Genetic Programming Theory and Practice, Kluwer Academic Publishers, Dordrecht (2003)
Howard, D.: Modularization by Multi-Run Frequency Driven Subtree Encapsulation. In: Genetic Programming Theory and Practice, Kluwer Academic Publishers, Dordrecht (2003)
Ryan, C., Keijzer, M., Cattolico, M.: Favorable Biasing of Function Sets Using Run Transferable Libraries. In: Genetic Programming Theory and Practice II, Kluwer Academic Publishers, Dordrecht (2004)
Zadeh, L.: From Computing with Numbers to Computing with Words – From Manipulation of Measurements to Manipulation of Perceptions. IEEE Transactions on Circuits and Systems 45, 105–119 (1999)
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Howard, D. (2007). Multiple Solutions by Means of Genetic Programming: A Collision Avoidance Example. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_63
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DOI: https://doi.org/10.1007/978-3-540-72458-2_63
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