A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions

In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions. While several functions have been developed and derived over the past decades, little information is available on the mathematical and visual description, range of suitability, and applications of many such functions. To bridge this knowledge gap, this review provides an exhaustive survey of more than 300 benchmark functions used in the evaluation of optimization and metaheuristics algorithms. This review first catalogs benchmark and test functions based on their characteristics, complexity, properties, visuals, and domain implications to offer a wide view that aids in selecting appropriate benchmarks for various algorithmic challenges. This review also lists the 25 most commonly used functions in the open literature and proposes two new, highly dimensional, dynamic and challenging functions that could be used for testing new algorithms. Finally, this review identifies gaps in current benchmarking practices and suggests directions for future research.

Can AI Chatbots Pass the Fundamentals of Engineering and Principles and Practice of Engineering Structural Exams?

The engineering community has recently witnessed the emergence of chatbot technology with the release of OpenAI ChatGPT-4 and Google Bard. While these chatbots have been reported to perform well and even pass various standardized tests, including medical and law exams, this forum paper explores whether these chatbots can also pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) exams. A diverse range of civil and environmental engineering questions and scenarios are used to evaluate the chatbots' performance, as commonly present in the FE and PE exams. The chatbots' responses were analyzed based on their relevance, accuracy, and clarity and then compared against the recommendations of the National Council of Examiners for Engineering and Surveying (NCEES). Our report shows that ChatGPT-4 and Bard, respectively scored 70.9% and 39.2% in the FE exam and 46.2% and 41% in the PE exam. It is evident that the current version of ChatGPT-4 could potentially pass the FE exam. While future editions are much more likely to pass both exams, this study also highlights the potential of using chatbots as teaching assistants and guiding engineers.