Governments, manufacturing, and technology companies are taking note of the power of GP and its unusual potential to generate viable inventions. Machine learning experts use genetic programming (GP) to solve difficult optimization problems and to create new inventions. GP is a machine learning algorithm that has the power to invent novel and surprising solutions to hard problems. In 2022, machine learning experts are driving the GP innovation trend by offering their expertise to many industries. Here we explore the future of GP as an “invention machine” and some of genetic programming’s real-life applications.
What is Genetic Programming?
GP’s powerful capabilities come from the fact that it imitates evolution in nature. Genetic programming is a subset of machine learning and a type of evolutionary algorithm (EA). GP involves using algorithms modeled on classic Darwinian evolution, as it occurs in nature.
The GP process begins with defined instructions, nodes (functions) and links (terminals) in syntax trees, which serve as the “chromosomes,” along with “fitness” functions and parameters for the run. There are also termination criteria, which outline the ideal outcomes of the program.
Genetic Programming Example
Unlike genetic algorithms, which produce raw data often in the form of strings with fixed lengths, GP programs are generated in any programming language, namely Lisp, MatLab, C++, and Java. If the run generates a viable solution, it is virtually ‘ready-to-go’ once the GP process terminates.
Part of the algorithm is a random initialization, a step by which the algorithm randomly generates possible solutions to the problem as a starting point. Then it combines these random models to improve the performance. Although the random process of evolution produces many nonsensical models, it also produces novel and innovative models that look ingenious to humans. Since models are generated out of randomness, GP practically invents solutions from nothing, which is why it has been dubbed the ‘invention machine.’
Examples of Genetic Programming: Real-Life GP Applications
GP adoption is increasing across various industries, including healthcare, automotive, and robotics. Already companies have seen success in their industries by utilizing GP to optimize and innovate on existing workflows, manufacturing processes, and forecasting models. Here are some of the ways in which companies are currently working with GP machine learning experts to drive innovation:
GP for Cancer Treatment Personalization
Cancer treatment requires detailed assessments and monitoring for all patients. With the healthcare worker shortage expected to impact the industry until at least 2026, companies are exploring ways to help cancer patients achieve better outcomes. At the same time, innovation is how providers will adapt to changing professional staffing resource expectations.
Genetic programming is currently being explored for use in developing personalized treatment plans for cervical cancer. Medical experts have suggested that treatment priorities be patient-specific. Through this technology, healthcare providers will be able to accurately prioritize and schedule priority rules for the patient’s cancer treatment plan. A study found an approach to treatment personalization utilizing GP delivered is preferred by medical specialists in 8 out of 10 cases.
Optimizing Automotive Engineering With GP
AI has already become a major trend in the transportation industry, with the number of IP filings related to artificial intelligence up 134% annually in 2021. With self-driving car technology at the focus of many automotive companies’ R&D budgets, companies are investing heavily in AI and machine learning IP.
Major car companies, such as BMW, are already involving genetic programming in their engineering process. BMW turned to genetic programming as a means to find solutions for improving automotive crash applications. Through GP, BMW is able to uncover the characteristics of automotive engineering optimization problems.
GP in Economic Forecasting and Modeling
Genetic programming can be used to generate economic forecasts and economic sentiment indicators. The main advantage of using GP in economic forecasting is that it does not require previous knowledge of the underlying interconnections and it offers flexibility. There are no restrictions on the models or parameters.
VILNIUS TECH published a 2019 study involving the use of genetic programming to estimate economic sentiment in the EU and the Baltic Countries. The study uncovered that, in most cases, an approach using genetic programming delivered superior performance compared to recursive autoregressive prediction models that are traditionally used to forecast economic growth.
GP in Robotics Navigation: Unexplored Environments
Robotic navigation is a major challenge for industries, especially where robots need to be deployed in unexplored environments, such as disaster incidents, underwater, or extra-terrestrial locations. Path planning and trajectory optimization through the use of genetic programming can help robots learn in the virtual domain to reduce errors made by the physical robot. By utilizing GP, manufacturers can design real-time robotic control applications that deliver greater efficiency and performance in high-level tasks.
Commercially Useful New Inventions (CUNI) From GP
GP can be used as an automatic “invention” machine in order to create commercially viable new inventions. Today’s inventions that come to market are primarily created by companies that have a vested interest in identifying commercially viable solutions to existing problems. However, with the addition of GP as an innovation tool, companies now have a new tool to add to their R&D arsenal. GP has the potential to deliver CUNI that companies can use to endlessly delight customers and keep them loyal to their brands.
“Programming The Unprogrammable” (PTU) With Genetic Programming
PTU involves using GP to drive the automatic formation of programs that are designed to work with unconventional computer devices. These are devices for which there are no existing precursors in any form that humans can use to create programs for these devices. GP can be used to create computer programs for unconventional computing devices including cellular automata, parallel programming systems, field-programmable gate arrays, distributed systems, and more.
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