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; AutoChef: Automated Generation of Cooking Recipes. Jabeen, H., Weinz, J., & Lehmann, J. In IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, United Kingdom, July 19-24, 2020, pages 1–7, 2020. IEEE. Link: https://ieeexplore.ieee.org/document/9185605 ----------------------------------------- 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Hajira Jabeen CEPLAS, BioCenter, University of Cologne Zülpicher Str. 47b 50674 Cologne, Germany hajira.jabeen@uni-koeln.de Jonas Weinz Kurfürstenstraße 11, 53115 Bonn Germany jo.we93@gmail.com Prof. Jens Lehmann Fraunhofer IAIS Zwickauer Straße 46 01069 Dresden Germany jens.lehmann@iais.fraunhofer.de ----------------------------------------- 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Hajira Jabeen ----------------------------------------- 4. the abstract of the paper(s); Cooking is an endeavour unique to humans. It is mainly considered an art requiring culinary intuition acquired through practice. The preparation of food is a complex and subjective process that makes it challenging to determine underlying rules for automation. In this paper, we present AutoChef, the first open-source autonomous recipe generator. AutoChef extracts the data from existing recipes using natural language processing, learns the combination of ingredients, preparation actions and cooking instructions, and autonomously generates the recipes. Furthermore, AutoChef uses Genetic Programming to represent and evolve the recipes. The fitness of recipes is designed to evaluate the combination of ingredients, actions and cooking processes learned from the existing recipe data. Finally, the resulting recipes are translated back into text format and evaluated by human experts. ----------------------------------------- 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. (C) The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. (G) The result solves a problem of indisputable difficulty in its field. ----------------------------------------- 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); B) AutoChef addresses the challenging problem of automatic generation of cooking recipes. The work presented here is an innovative scientific result. The tasks of recipe generation and the use of genetic programming have never been attempted before. (C) There are no published recipe generation/evolution works, apart from the same authors. The results presented in this paper are better than those published online mentioned below: -“IBM Chef-Watson”, often resulted in hard to find ingredients and the project seems to be canned now and the link is no longer functional. [http://www.ibmchefwatson.com] -Evolutionary Meal Management Algorithm (EMMA) generated the recipes using machine learning algorithms. The naive recipes often lack instructions or quantities.[https://covercheese.appspot.com/ ] (F) No similar work exists in the literature. AutoChef is a novel piece of work. (G) The possible number of ingredients used to prepare recipes is very large. If we combine these with the possible herbs and spices that can be added, as well as several cooking actions and processing methods, recipe-making becomes complex. Recipe generation can be seen as a complex combinatorial optimization problem. In addition to handling this problem, Autochef addresses the challenge of designing a structure to represent recipes as a genetic program, that can represent recipes, associated actions, methods and ingredients. This genetic program can be evolved to generate valid, new, and better programs which can be transformed into understandable text illustrating a valid, human competitive recipe. Moreover, AutoChef addresses the problem of measuring the fitness of a recipe. In this work, we have extracted and used the statistical information extracted from recipes to define the number of ingredients, steps, heating actions and their combinations, from existing data. Given more data, the fitness function can be tailored to address additional objectives like cost or nutrients. ----------------------------------------- 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Jabeen, H., Weinz, J., & Lehmann, J. (2020, July). AutoChef: Automated Generation of Cooking Recipes. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-7). IEEE ----------------------------------------- 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; Prize money, if any, is to be divided equally among co-authors. ----------------------------------------- 9. a statement stating why the authors expect that their entry would be the "best," 1:-Although cooking is one of the key indicators of civilised human life, not many people know cooking. Nonetheless, the need for cooking and the importance of a good recipe is known to almost everyone. Given the fact that not all people can cook, our system almost certainly performs better than some humans 2:-Autochef can search a large number of possible ingredient combinations to develop novel combinations by learning from the existing recipe data, which is not humanly possible. 3:AutoChef can search in different regional (valid and tested combinations) ingredients and couple them with spices, processing-actions and heating methods to create new recipes. This might be unknown to a typical home-cook. In summary, AutoChef presents the first fully automated system to develop human competitive cooking recipes. In order to achieve this, we have developed the system to learn from recipe data; a) the list of ingredients, b) the cooking actions, c) ingredient-action combinations, d) ingredient-ingredient combinations. We used this information to develop a genetic program that can represent recipes as a tree. We evolved these recipes through specialised operators to create new recipes. These recipe trees, not only represent valid and edible recipes but can also be translated into understandable text. We addressed the challenge of fitness evaluation of new recipes by exploiting statistics computed from the existing recipes. To the best of our knowledge, AutoChef is the first successful attempt to generate and evolve valid, understandable, and human competitive recipes automatically Our automated generation of novel recipes offers interesting opportunities for the Food-For-Future. a) Searching for infrequent ingredients-spice-actions can help transform a boring food into an interesting meal. b) Given sufficient data, the fitness function can be adjusted to create recipes fulfilling particular requirements like diet, effort, cost, or nutrition. This can allow possibilities to search for more filling food to address hunger or weight loss. ----------------------------------------- 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. * GP (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 the press" requirement. *Jabeen, H., Weinz, J., & Lehmann, J. (2020, July). AutoChef: Automated Generation of Cooking Recipes. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-7). IEEE