Non-Darwinian Evolutionary Computation: Guiding Evolution by Machine Learning

点击翻译按钮获取中文摘要

This is the first year funding of a three year continuing award. This project concerns a new approach to evolutionary computation, called Learnable Evolution Model (LEM), which draws inspiration from "intellectual evolution" - an evolution of human ideas, designs, artifacts, organizations, etc. Unlike conventional Darwinian-type evolutionary algorithms, in which new candidate solutions are created by semi-random mutations and/or recombinations, LEM generates new solutions by hypothesis formation (via machine learning) and hypothesis instantiation (via deduction). Pilot studies have shown that LEM can accelerate evolutionary processes by two or more orders of magnitude over Darwininan-type algorithms (in terms of the number of births or fitness evaluations), and thus appears to be particularly advantageous in application domains in which fitness evaluations are time-consuming and/or costly, such as engineering optimization problems, evolutionary design, fluid dynamics, drug development, evolvable hardware, geological inversion, etc. This research will explore LEM's capabilities, limitations, and scalability to complex optimization and engineering design tasks. The PI will develop extensions of the initial methodology, and will experimentally test LEM in selected real-world problem domains.