(1) the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result, Enhanced Optimization With Composite Objectives and Novelty Pulsation (2) the name, complete physical mailing address, e-mail address of EACH author, Hormoz Shahrzad Email: hormoz@cognizant.com +1(925)487-1481 Babak Hodjat Email: babak@cognizant.com Camille Dolle Email: camille.dolle@sentientim.com Andrei Denissov Email: andrei.denissov@sentientim.com Simon Lau Email: simon.lau@sentientim.com Donn Goodhew Email: donn@sentientim.com Justin Dyer Email: justin.dyer@sentientim.com Risto Miikkulainen Email: risto@cs.utexas.edu Physical address for all: 649 Front Street, Suite #3 San Fransisco, CA 94111, USA (3) the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition), Hormoz Shahrzad (4) the abstract of the paper(s), An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. A recent solution is to replace the original objectives by their linear combinations, thus focusing the search on the most useful trade-offs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. This paper improves this approach further by introducing novelty pulsation, i.e. a systematic method to alternate between novelty selection and local optimization. In the highly deceptive problem of discovering minimal sorting networks, it finds state-of-the-art solutions significantly faster than before. In fact, our method so far has established a new world record for the 20-lines sorting network with 91 comparators. In the real-world problem of stock trading, it discovers solutions that generalize significantly better on unseen data. Composite Novelty Pulsation is therefore a promising approach to solving deceptive real-world problems through multi-objective optimization. (5) a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies, A, B, D, E, F, G. (6) a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission), A sorting network of n inputs is a fixed sequence of comparison-exchange operations (comparators) that sorts all inputs of size n. Minimizing the number of comparators required for a given n is a hard combinatorial optimization problem that has been the subject of active research since the 1950's, when O'Conner and Nelson constructed networks for 4 <= n <= 8 (U.S. Patent 3029413). Their networks had the minimal number of comparators for 4, 5, 6, and 8 inputs, but required two extra comparators for 7 inputs. This result was improved by Batcher (1968), whose algorithmic construction produces provably minimal networks for n <= 8 (Knuth, 1998). Still today, provably minimal networks are known only for n <= 10. Finding the minimum number of comparators for n > 10 is thus a challenging open problem. It has been studied by various researchers using specialized techniques, often separately for each value of n. For example, the 16-input case, for which Batcher's method requires 63 comparators, was improved by Shapiro who found a 62-comparator network in 1969. Soon afterward, Green found a network with 60 comparators, which still remains the best in terms of the number of comparators. Knuth (1998) documents the interesting history of how advances were made for the other values of n and how "each step was forged with some difficulty". In short, networks designed using human ingenuity are still the best known solutions for 9 <= n <= 16 (except for n = 13; Juille, 1995). For larger values of n, all the best known solutions are simply merges of smaller networks; the problem is so difficult that nobody has been able to improve on these straightforward constructions (Baddar, 2009). Our approach was developed as part of research into evolving solutions to complex/deceptive problem of stock trading by combining multi-objective optimization with novelty search, and as a good benchmark for deceptive domains we picked sorting networks to validate the method and without any special tuning/customization, it matched the state of the art for n <= 19 and unexpectedly improved the state of the art result for n = 20. Therefore, our result satisfy the following criteria for human competitiveness: (A) Minimal networks for the simpler 4 <= n <= 8 cases are patented (U.S. Patent 3029413); the current result extend these results to a more difficult case (n = 20), and would therefore qualify today as a patentable new invention. (B) Knuth (1998) and Koza et al. (1999) have surveyed many results on minimal sorting networks that have been published in peer-reviewed scientific journals during the last several decades. Our results are equal to these results for n <= 19 and is better for n = 20. (D) Since the result improves the upper bounds on network size for n = 20, it is publishable in its own right as new scientific results independent of the fact that the results were mechanically created. (E) Knuth (1998) and Koza et al. (1999) describe how a succession of increasingly better sorting networks for n <= 16 were designed by humans starting in the 1950's. Our results are equal to the most recent such solutions up to n = 19 and is better for n = 20. (F) The previous results that reduced the number of comparators were all considered achievements in the field since they improved the best known upper bounds on network size. The current results are equal to these results for n <= 19 and is better for n = 20. (G) Even after more than half-century of intensive research, finding minimal size sorting networks for n > 10 remains an open problem of indisputable difficulty in Computer Science. The significance of our approach is its generality in a sense that it doesn't utilize any domain specific knowledge to constrain evolutionary search, matching all the rsults for n <= 19 and producing a better result for n = 20. (7) a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); H. Shahrzad, B. Hodjat, C. Dolle, A. Denissov, S. Lau, D. Goodhew, J. Dyer, and R. Miikkulainen. Enhanced Optimization with Composite Objectives and Novelty Pulsation. In: Genetic Programming Theory and Practice XVII, Michigan State University, East Lansing, MI, USA, May 16-19, 2019. (8) a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; and Any prize money, if any, is to be divided equally among the co-authors. (9) a statement stating why the judges should consider the entry as "best" in comparison to other entries that may also be "human-competitive." First, our evolved result improve upon half-century of results published in patents, books, and peer-reviewed literature, making the result highly significant scientifically. They show that evolutionary methods can be used to discover solutions to design problems that have fascinated theorists for a long time. Second, the solutions are important in practice. Sorting networks implement oblivious, i.e. data-independent sorting, where the sequence of comparisons does not depend on input data. Therefore, they are used to implement sorting in parallel hardware e.g. on graphics processing units (Kipfer et al., 2004) and in switching networks (Batcher, 1968). Smaller such networks make such applications more efficient. Third, the method we have used is a general method which we originally developed for application in stock trading domain and then we applied it to sorting network without any modification/customization as a toy domain just to validate its effectiveness, despite tailoring a method that utilizes the domain specificities like Valsalam, V. K. 2010. (10)Type of EC used Genetic Programming (11)The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. This is the link to GPTP-2019 presentation schedule of accepted papers: http://gptp-workshop.com/schedule.html {Saturday 18 May, 2019 12:15p Hormoz Sharhzad et al. “Enhanced Optimization with Composite Objectives and Novelty Pulsation”} REFERENCES Valsalam, V. K. (2010). "Utilizing Symmetry in Evolutionary Design". PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX. Technical Report AI-10-04. http://nn.cs.utexas.edu/?valsalam:phd10 Batcher, K. E. (1968). Sorting networks and their applications. In AFIPS Spring Joint Computing Conference, 307--314. Baddar, S. W. A. (2009). Finding Better Sorting Networks. PhD thesis, Kent State University. Juille, H. (1995). Evolution of non-deterministic incremental algorithms as a new approach for search in state spaces. In Proceedings of the 6th International Conference on Genetic Algorithms, 351--358. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Kipfer, P., Segal, M., and Westermann, R. (2004). Uberflow: A gpu-based particle engine. In HWWS '04: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, 115--122. New York, NY, USA: ACM. Knuth, D. E. (1998). Art of Computer Programming: Sorting and Searching, vol. 3. Addison- Wesley Professional. Second edition. Koza, J. R., Bennett III, F. H., Andre, D., and Keane, M. A. (1999). Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann Publishers. O'Connor, D. G. and Nelson, R. J. (1962). Sorting System with N-Line Sorting Switch. United States Patent number 3,029,413. Filed Feb 21, 1957. Issued Apr 10, 1962.