GALE- Geometric Active Learning for Search-Based Software Engineering
with Joseph Krall,, WVU
Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When MOEAs explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.