Self-Adaptive / Self-Tuning Metaheuristics

Genetic algorithms, and more generally, evolutionary computation, along with other metaheuristics such as simulated annealing, ant colony optimization, etc, often involve a large number of so-called control parameters (e.g., mutation and crossover rates in genetic algorithms, parameters to control the cooling schedule in simulated annealing, and so forth). Getting these control parameters "right" is often critical to search performance. My research in this area includes both automated parameter tuning, as well as self-adaptive metahueristics or automated parameter control. My research in this area also includes multi-heuristic search. Some forms of search require the guidance of a heuristic, and the "best" heuristic for a problem may vary widely from that of another problem, or may even vary across instances of a problem. Some of my research has applied machine learning to enabling metaheuristics to adapt their choice of heuristic.

Selected Publications