ABSTRACT
In the course of an evolutionary optimization, solutions are often generated with low phenotypic fitness even though the corresponding genotype may be close to an optimum. Without additional information about the local fitness landscape, such genetic near misses would be overlooked under strong selection. Presumably, one could rank near misses by performing a local search and scoring them according to distance from the nearest optimum. Such evaluations are essentially the goal of hybrid algorithms (Chapters 11-13, Balakrishnan and Honavar 1995), which combine global search using evolutionary algorithms and local search using individual learning algorithms. Hybrid algorithms can exploit learning either actively (via Lamarckian inheritance) or passively (via the Baldwin effect).
