An experimental study of adaptive control for evolutionary algorithms

作者：di Tollo Giacomo^*; Lardeux Frederic; Maturana Jorge; Saubion Frederic

来源：Applied Soft Computing, 2015, 35: 359-372.

DOI：10.1016/j.asoc.2015.06.016

• Algorithms
• Design experimentation
• Measurement
• Performance
• Evolutionary algorithms
• Adaptive operator selection

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.

• 出版日期2015-10