Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.Comment: 9 pages, 8 figures, GECCO 201

Local Search is Underused in Genetic Programming

Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.authorsversionpublishe

Visibility and digital art: Blockchain as an ownership layer on the Internet

Visibility of digital art and its ownership can be achieved using blockchain technology as part of a broader solution for the identification, attribution, and payment for digital work. A case study is provided of a firm using the Bitcoin blockchain as part of an integrated solution to identify and authenticate ownership of digital property. An integrated ownership ledger allows for secure attribution, transfer, and provenance of digital property. Blockchain technology enables limited-edition digital property, while Internet-scale web crawl and machine learning shows where and how works are being used on the Internet

Importance Sampled Circuit Learning Ensembles for Robust Analog IC Design

ABSTRACT This paper presents ISCLEs, a novel and robust analog design method that promises to scale with Moore's Law, by doing boosting-style importance sampling on digital-sized circuits to achieve the target analog behavior. ISCLEs consists of: (1) a boosting algorithm developed specifically for circuit assembly; (2) an ISCLEs-specific library of possible digital-sized circuit blocks; and (3) a recently-developed multi-topology sizing technique to automatically determine each block's topology and device sizes. ISCLEs is demonstrated on design of a sinusoidal function generator and a flash A/D converter, showing promise to robustly scale with shrinking process geometries

Massively multi-topology sizing of analog integrated circuits

This paper demonstrates a system that performs multi-objective sizing across 100,000 analog circuit topologies simultaneously, with SPICE accuracy. It builds on a previous system, MOJITO, which searches through 3500 topologies defined by a hierarchically-organized set of 30 analog blocks. This paper improves MOJITO's results quality via three key extensions. First, it enlarges the block library to enable symmetrical transconductance amplifiers and more. Second, it improves initial topology diversity via optimization-based constraint satisfaction. Third, it maintains topology diversity during search via a novel multi-objective selection mechanism, dubbed TAPAS. MO-JITO+TAPAS is demonstrated on a problem with 6 objectives, returning a tradeoff holding 17438 nondominated designs. The tradeoff is comprised of 152 unique topologies that include the newly-introduced topologies. 59 designs across 12 topologies designs outperform an expert-designed reference circuit.status: publishe