Claes Strannegård, Nils Svangård, David Lindström, Joscha Bach and Bas Steunebrink
International joint conference on artificial intelligence (IJCAI), 4246.
Jonsson, A., and Barto, A. G. 2001. Automated state abstraction for options using the U-tree algorithm. In Advances in neural information processing systems, 1054-1060.
Keramati, M., and Gutkin, B. S. 2011. A reinforcement learning theory for homeostatic regulation. In Advances in neural information processing systems, 82-90.
Langton, C. G. 1997. Artificial life: An overview. MIT Press.
LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep
-Simon Scale. Number 11. Williams & Wilkins Company.
Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York
Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112.
Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre
.; Schorlemmer, M.; and Smaill, A., eds., Computational Creativity Research: Towards Creative Machines , Atlantis Thinking Machines. Atlantis-Springer. chapter 12, 243–266.
Lerdahl, F., and Jackendoff, R. 1983. A generative theory of tonal music . Cambridge. MA: The MIT Press.
Manurung, H. 2003. An evolutionaryalgorithm approach to poetry generation . Ph.D. Dissertation, University of Edinburgh.
Misztal, J., and Indurkhya, B. 2014. Poetry Generation System With an Emotional Personality. In Proceedings of 5th International Conference on Computational
, N. A. 2016. Alien Mindscapes – A Perspective on the Search for Extraterrestrial Intelligence. Astrobiology 16:661–676.
Carnap, R. 1950. Logical Foundations of Probability . Chicago: The University of Chicago Press.
Cohen, P. R. 2005. If Not Turing’s Test, then what? AI Magazine 26:61–67.
Davis, R. 1998. What are intelligence? and why? 1996 AAAI Presidential Address. AI Magazine 19(1):91–111.
Domingos, P. 2018. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World . New York, NY, USA: Basic Books
.; and et al. 2004. Computation Within Cultured Neural Networks. Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society .7, 5340-5343.
Dewar, C.R. 2009. Maximum Entropy Production as an Inference Algorithm that Translates Physical Assumptions into Macroscopic Predictions: Don’t Shoot the Messenger. Entropy. 11(4), 931-944.
Di Caprio, D.; Badiali, J.P.; and Holovko, M. 2008. Simple field theoretical approach of Coulomb systems. Entropic effects. Available electronically from http
 Clark D. M. Evolution of algebraic terms 1: term to term operation continuity. International Journal of Algebra and Computation, 23(05):1175-1205, 2013.
 Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002.
 Ficici S. G. and Pollack J. B. Pareto optimality in coevolutionary learning. In Kelemen J. and Sosík P., editors, Advances in Artificial Life, 6th European Conference, ECAL 2001, volume 2159
 Auger, A., Hansen, N., Performance evaluation of an advanced local search evolutionaryalgorithm, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2, 2005, 1777-1784.
 Benson, H.Y., Shanno, D.F., Vanderbei, R.J., Interior-point methods for nonconvex nonlinear programming: Jamming and comparative numerical testing, Operations Research and Financial Engineering, Princeton University, Technical Report ORFE-00-02, 2000.
 Billups, S.C., Dirkse, S.P., Ferris, M.C., A comparison of algorithms for large-scale mixed
, Springer, Berlin 2008.
 Gao J., Gen M., Sun L. A hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problem, GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation , ACM 2006, 1158.
 Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning , Dorling Kindersley Pvt Ltd, New Delhi 2008, 17.
 Grimes D., Hebrard E., Model and strategies for variants of the job shop scheduling problem, CP'11: Proceedings of the 17th
Algorithm using Cuckoo Search and Differential Evolution for Data Clustering. Indian Journal of Science and Technology , 8 , 24, 2015.
 Changhe, L. and Y. Shengxiang. A clustering particle swarm optimizer for dynamic optimization. in Evolutionary Computation, 2009. CEC '09. IEEE Congress on .2009, 439-446.
 Chen, C.-Y. and Y. Fun. Particle swarm optimization algorithm and its application to clustering analysis. in Networking, Sensing and Control, 2004 IEEE International Conference on .2004, 789-794 Vol.2.
 Chuang, L.-Y., C.-J. Hsiao, and C
Atefeh Moghaddam, Jacques Teghem, Daniel Tuyttens, Farouk Yalaoui and Lionel Amodeo
 Y. Bartal, S. Leonardi, A. Marchetti-Spaccamela, J. Sgall, L. Stougie, “Multiprocessor scheduling with rejection”, Journal of Discrete Mathematics 13(1), 64–78, (2000).
 J. Blazewicz, K.H. Ecker, E. Pesch, G. Schmidt, J. Weglarz, “Handbook on scheduling : From theory to applications”, Springer Berlin Heidelberg New York (2007).
 K. Deb, S. S. Agrawal, A. Pratap, T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, Lecture Notes in Computer Sciences , 1917, 849