The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm
is likely to perform best for my problem? Recognizing the problem as a learning task in the …

In the past, the notion of fitness landscapes has found widespread adoption. Many different
methods have been developed that provide a general and abstract framework applicable to …

Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

In evolutionary computation, the fitness function normally measures progress toward an
objective in the search space, effectively acting as an objective function. Through deception …

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …

Research on a new metaheuristic for optimization is often initially focused on proof-of-
concept applications. It is only after experimental work has shown the practical interest of the …

Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for
attacking hard combinatorial optimization problems. Yet, its performance, when compared to …

The problem of complex adaptations is studied in two largely disconnected research
traditions: evolutionary biology and evolutionary computer science. This paper summarizes …

This paper describes GenProg, an automated method for repairing defects in off-the-shelf,
legacy programs without formal specifications, program annotations, or special coding …