Abstract
In the conceptional phases of design optimization tasks it is required to find new innovative solutions to a given problem. Although evolutionary algorithms are suitable methods to this problem, the search of a wide range of the solution space in order to identify novel concepts is mainly driven by random processes and is therefore a demanding task, especially for high dimensional problems. To improve the exploration of the design space additional criteria are proposed in the presented work which do not evaluate solely the quality of a solution but give an estimation of the probability to find alternative optima. To realize these criteria, concepts of novelty and interestingness are employed. Experiments on test functions show that these novelty guided evolution strategies identify multiple optima and demonstrate a switching between states of exploration and exploitation. With this we are able to provide first steps towards an alternative search algorithm for multi-modal functions and the search during conceptual design phases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bentley, P.J., Corne, D.W. (eds.): Creative Evolutionary Systems. Morgan Kaufmann, San Francisco (2001)
Shir, O.M., Bäck, T.: Niching in evolution strategies. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 915–916. ACM, New York (2005)
Shir, O.M.: Dynamic niching in evolution strategies with covariance matrix adaptation. In: Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005, Piscataway, pp. 2584–2591. IEEE Press, Los Alamitos (2005)
Shir, O.M., Bäck, T.: Niche radius adaptation in the cma-es niching algorithm. In: Parallel Problem Solving from Nature (PPSN IX), Reykjavik, Iceland (2006)
Herdy, M.: Evolution strategies with subjective selection. In: PPSN IV: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, London, UK, pp. 22–31. Springer, Heidelberg (1996)
Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE 89, 1275–1290 (2001)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8, 970–974 (1996)
Schmidhuber, J.: What’s interesting? Idsia-35-97, IDSIA, Switzerland (1997)
Saunders, R.: Curious Design Agents and Artificial Creativity. PhD thesis, Faculty of Architecture, The University of Sydney (2001)
Risi, S., Vanderbleek, S.D., Hughes, C.E., Stanley, K.O.: How novelty search escapes the deceptive trap of learning to learn. In: GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 153–160. ACM, New York (2009)
Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference on Artificial Life (ALIFE XI). MIT Press, Cambrige (2008)
Oudeyer, P.Y., Kaplan, F.: What is intrinsic motivation? a typology of computational approaches. Frontiers in Neurorobotics (2007)
Bishop, C.M.: Novelty detection and neural network validation. In: Proc. IEE Conference on Vision and Image Signal Processing, pp. 217–222 (1994)
Igel, C., Husken, M.: Improving the rprop learning algorithm. In: Second International Symposium on Neural Computation, pp. 115–121 (2000)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature, pp. 849–858. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Graening, L., Aulig, N., Olhofer, M. (2010). Towards Directed Open-Ended Search by a Novelty Guided Evolution Strategy. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)