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. The automatic design of multi-objective ant colony optimization algorithms. 2. The name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper: Manuel López-Ibáñez IRIDIA, Université Libre de Bruxelles (ULB), Av. F. Roosevelt 50, CP 194/6, 1050 Brussels, Belgium. manuel.lopez-ibanez@ulb.ac.be +32 (0) 2650 2745 Thomas Stützle IRIDIA, Université Libre de Bruxelles (ULB), Av. F. Roosevelt 50, CP 194/6, 1050 Brussels, Belgium. stuetzle@ulb.ac.be +32 (0) 2650 3167 3. The name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition) Manuel López-Ibáñez (manuel.lopez-ibanez@ulb.ac.be) 4. The abstract of the paper(s) Multi-objective optimization problems are problems with several, typically conflicting criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multi-objective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multi-objective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multi-objective algorithms. 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 B The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. D The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created. E The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. 6. A statement stating why the result satisfies the criteria that the contestant claims. Since the proposal of the ant colony optimization (ACO) metaheuristic in the 1990s, there is an ongoing research on applying the ACO metaheuristic to multi-objective optimization problems. This interest has resulted on a dozen competing proposals on how to design such multi-objective ACO (MOACO) algorithms. The different MOACO designs share common ideas but are also distinguished by alternative choices for various algorithmic components. In fact, it is possible to design hundreds of "new" MOACO algorithms by merely combining in novel ways the algorithmic components already proposed in the literature. Following this idea, we have build a parametrized framework that allows us to instantiate new designs of MOACO algorithms. However, instead of deciding by ourselves what would be the best design for a given problem, we let an automatic configuration tool to choose the best parameters of the framework given a set of training problem instances. Our approach is the first that automatically configures heuristic multi-objective optimizers. The proposed framework, from which MOACO algorithm designs are instantiated, has overall 16 configurable parameters of which 7 categorical and 3 numerical directly affect the design choices for the MOACO algorithm. The result of our automatic design procedure is a MOACO algorithm that outperforms at least nine MOACO algorithms from the literature, even after the numerical parameters of those algorithms are tuned in order to improve their performance. Therefore, our automatic design procedure satisfies the following criteria: (B) Our automatically designed MOACO algorithm is better than nine other human-designed MOACO algorithms, which were accepted as a new scientific result at the time when they were published in a peer-reviewed scientific journal. (D) Our result was published, in the prestigious IEEE Transactions on Evolutionary Computation, in its own right as a new scientific result independent of the fact that the result was mechanically created. (E) Our result completely outperforms the best human-created MOACO algorithm for the bi-objective TSP. García-Martínez et al. (2007) compared ten human-designed MOACO algorithms and found BicriterionAnt to be the best for the bi-objective TSP. Our automatically designed MOACO algorithm completely outperforms BicriterionAnt, even after we tuned the parameters of the latter to further improve its performance. C. García-Martínez, O. Cordón, and F. Herrera. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research, 180 (1):116–148, 2007. 7. A full citation of the paper M. López-Ibáñez and T. Stützle. The automatic design of multi-objective ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation, 16(6):861–875, 2012. doi:10.1109/TEVC.2011.2182651. 8. 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." Let us state the reasons why the judges may consider the entry to be "not the best", and we will reply to each of these reasons: * "The design of MOACO algorithms for the bi-objective TSP is a lesser problem compared to other problems related to health, energy or the economy." Our answer is that the problem we tackled is a representative case of the more general problem of designing complex algorithms from a large number of potential components. The facts that almost all human-designed MOACO algorithms are included in our comparison, and that we chose a simple problem for which recent independent work is available, make our conclusions more robust than if we had chosen a more complex problem for which a definition of "best" would be hard to establish. In addition, we provide [http://iridia.ulb.ac.be/~manuel/moaco] the problem instances, the source code of the algorithmic framework, and the source code of the automatic configuration tool used in our study, which would allow anyone to replicate our results. * "The idea of automatically designing algorithms is not new." Although Genetic Programming, Grammatical Evolution, and similar approaches also have the goal of automatically designing algorithms, our method is radically different. We make use of the knowledge and imagination already available in the literature in the form of alternative design choices, but we let an automatic procedure decide which design choices should be integrated into the final algorithm and how. In some sense, the humans contribute their imagination and inventiveness in the form of potential algorithmic designs, and the machine provides an unbiased selection among those designs based on a large amount of experimental data. The real contribution of our work is to show that this approach is not only feasible, but it clearly surpasses the capabilities of human designers. We believe this should become the standard way to design optimization algorithms in the future. * "There is no indication that this method works besides this case study." On the contrary, there is increasing evidence that the method used in our paper leads to new algorithm designs that surpass those designed by humans. Dubois-Lacoste et al. (2011) have used our method to further improve the state-of-the-art algorithm for various bi-objective permutation flowshop problems. The new design is not very different from the previous state-of-the-art algorithm, however, the fact that there is an improvement at all is significant. KhudaBukhsh et al. (2009) followed a similar method to automatically build solvers for the SAT competition, outperforming several human-designed algorithms. However, their work was targeted for algorithms that tackle a single objective problem. Our work is the first that considers the automatic configuration of multi-objective optimizers where numerical and categorical parameters, which are related to algorithm design, are considered in the automatic configuration process. J. Dubois-Lacoste, M. López-Ibáñez, and T. Stützle. Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pages 2019–2026. ACM Press, New York, NY, 2011. doi:10.1145/2001576.2001847. A. R. KhudaBukhsh, L. Xu, H. H. Hoos, and K. Leyton-Brown, "SATenstein: Automatically building local search SAT solvers from components," in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), 2009, pp. 517–524. Given the above arguments, we believe that our work is not only human-competitive, but rather it successfully replaces the human component from a key step on the design of algorithms leading to better results.