Last Friday, in our weekly meeting, the paper by Di Tollo et al. “From Adaptive to More Dynamic Control in Evolutionary Algorithms” was presented and discussed. This work in centered on the adaptation of application rates of different types of crossover. A performance function is defined that takes into account the quality of solutions and diversity generated by each crossover. By varying a user-defined variable (teta), the importance of each factor can be regulated in order to set the desired compromise between quality and diversity (which gives rise to the idea of applying a multi-objective approach here). Then, after credit assignment (for each crossover), an operator is selected by Probability Matching (PM) or Multi-Armed Bandit (MAB) strategies.
For testing the proposed scheme, the authors define a framework with 20 different crossover operators of which the main characteristics are known (i.e., whether they favor intensity/exploitation or diversification/exploration). The system is applied to SAT problems. Several conclusions are drawn from those simple experiments. First, the type of SAT problem greatly influences the behavior of the system, as well as the criteria used to compute the performance of the operator and the selection strategy (PM or MAB). That is, setting teta to a hypothetical compromise value between intensification and diversification leads to a variety of different behaviors and not necessarily to that expected compromise.
The second part of the experiments is focused on the dynamic variation of teta. The authors conclude that the variation strategy influences the behavior of the algorithm and the progress of the system. They also conclude that, in fact, it is possible to favor diversity-oriented crossover or quality-oriented crossover by tuning teta. That is, it is possible to control the desired features by changing the teta value during the search.
GeNeura Team 