Push tutorial accepted for presentation at GECCO-2021

[lspector]

Description:

Push is a general purpose, multi-type, Turing complete programming language for programs that evolve in an evolutionary computation system. Initially developed in 2000, it has been used for a range of research projects and applications, including the production of human-competitive results and state-of-the-art work on general program synthesis.

Push and systems that evolve Push programs (such as PushGP) are available in a variety of host languages. However, the core concepts of Push are simple enough that programmers should also be able to build their own Push systems relatively quickly, and to incorporate them into their own evolutionary computation systems. This tutorial will present examples and best practices that will help participants to use Push effectively in their own work, whether they use existing libraries or develop their own.

Push has unusually simple syntax, which makes it particularly easy to randomly generate and vary valid programs. It nonetheless supports rich data and control structures, which in most other languages involve syntax restrictions that can be difficult to maintain during evolutionary change. Furthermore, the data and control structures used by a Push program can themselves emerge through the evolutionary process.

This tutorial will provide a detailed introduction to the Push programming language, and will demonstrate the use of the PushGP genetic programming system with open source libraries in several host languages. Participants will be invited to install and interact with these libraries, and also to interact with online Push systems in their browsers during the tutorial.

Among the features of Push that will be illustrated are those that support the evolution of programs that use multiple types, iteration, recursion, and modularity to solve problems that may involve multiple tasks. Push-based “autoconstructive evolution” systems, in which evolutionary methods co-evolve with solutions to problems, will also be briefly described.

[thelmuth]

Yay

@williamlacava’s and my lexicase tutorial was also accepted! Here’s the description:

Lexicase parent selection decides which individuals in an EC population are selected not based on a single, aggregated fitness value, but instead based on random sequences of test cases (or objectives) used to filter the population down to a single individual. By never aggregating performance on separate cases, lexicase selection effectively makes use of additional information for parent selection. In Genetic Programming, this results in a semantic-aware parent selection, although lexicase selection has proven useful in other EC systems without a genotypic/semantic distinction. Since lexicase selection places importance on performing well on subsets of the test cases rather than generally on all cases at once, it often selects specialist individuals who perform well on some parts of a problem while poorly on others. These specialists contribute to the maintenance of increased diversity in populations evolved under lexicase selection. However, unlike some methods that focus entirely on diversity, lexicase selection also provides strong selection pressure toward individuals that perform well on many combinations of test cases, which can lead to excellent overall performance.

Lexicase selection has recently been used in various evolutionary computation systems, often outperforming tournament selection as well as other more advanced parent selection techniques. While originally formulated for Genetic Programming, it has also been used for evolutionary many-objective optimization in Genetic Algorithms, in Learning Classifier Systems, in Evolutionary Robotics, and as a method of building machine learning ensembles. Many variants of lexicase selection have been proposed to tackle different issues. In particular, epsilon lexicase selection has performed well in problem domains where errors on test cases are real numbers. We will discuss these variants and when you might want to use them.

This tutorial will give an introduction to lexicase selection and will discuss major research directions involving both advances in its use and explanations for why it performs well. We will discuss its effects on population diversity, its ability to select specialist individuals, and its propensity toward hyperselection, where it selects the same individual many times in one generation. Participants will come away with a strong understanding of lexicase selection and how it can be applied to any test case-based evolutionary computation system. In addition, they will gain insight into the research frontiers in lexicase selection.