Evolutionary Module Acquisition
Peter J. Angeline, Jordan B. Pollack · 1993
Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time. 1. Introduction Evolutionary programming (EP) (Fogel 1992; Fogel et. al. 1966) and genetic algorithms (GAs) (Holland 1966; Goldberg 1989) have borrowed little from each other. But there are many levels at which EP and GAs are similar. For instance, both employ an analogy to natural selection over a population to search through a sp...