Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms
This addresses a specific issue in evolutionary computation for researchers, but appears incremental as it builds on existing adaptive methods.
The paper tackled the problem of genetic algorithms getting trapped in local optima on complex fitness landscapes by proposing adaptive mechanisms for mutation rate and flood mechanisms, though no concrete results or numbers are provided.
This paper concerns applications of genetic algorithms and genetic programming to tasks for which it is difficult to find a representation that does not map to a highly complex and discontinuous fitness landscape. In such cases the standard algorithm is prone to getting trapped in local extremes. The paper proposes several adaptive mechanisms that are useful in preventing the search from getting trapped.