Abstract:
The Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM "genetically engineers" new populations via hypothesis formation and instantiation. This paper presents results from new studies in which LEM was systematically tested on a range of optimization problems and a complex real world design task. The study involved LEM2, a new implementation oriented toward function optimization, and LEMd-ISHED, an implementation oriented toward engineering design. LEM2 strongly outperformed tested evolutionary algorithms in terms of the evolution length, measured by the number of fitness function evaluations needed to reach the solution. This evolutionary speedup also translated to an execution speedup whenever the fitness evaluation time was above a small threshold (a fraction of a second). Experiments with LEMd-ISHED on problems of optimizing heat exchangers (evaporators) produced designs that matched or exceeded designs produced by human experts.
