C2.7 Generation Gap Methods C2.7.1 Introduction C2.7.2 Historical Perspective

In this section, we will examine how generation gap methods are related to the concept of overlapping and non-overlapping populations in evolutionary algorithms (EAs). We will then review why generation gap methods were designed. We will compare and contrast the generational and steady state systems. Finally we will describe the elitist strategies used in evolutionary algorithms. The concept of generation gap is linked to the notion of non-overlapping and overlapping populations. In a non-overlapping model parents and oospring never compete with one another, i.e., the entire parent population is always replaced by the oospring population, while in an overlapping system, parents and oospring compete for survival. The term generation gap refers to the amount of overlap between parents and oospring. The notion of a generation gap is closely related to selection algorithms and population management issues. A selection algorithm in an evolutionary algorithm (EA) involves two elements: (1) a selection pool and (2) a selection distribution over that pool. A selection pool is required for reproduction selection as well as for deletion selection. The key issue in both these cases is \what does the pool contain when parents are selected and when survivors are selected?" In the selection for reproduction phase, parents are selected to produce oospring and the selection pool consists of the current population. How the parents are selected for reproduction depends on the individual EA paradigm. In the selection for deletion phase, a decision has to be made as to which individuals to select for deletion to make room for the new oospring. In non-overlapping systems the entire selection pool consisting of the current population is selected for deletion, i.e., the parent population () is always replaced by the oospring population (). In overlapping models, the selection pool for deletion consists of both parents and their oospring. Selection for deletion is performed on this combined set and the actual selection procedure varies in each of the EA paradigms. Historically, both evolutionary programming and evolution strategies had overlapping populations while the canonical genetic algorithms used non-overlapping populations. In evolutionary programming (Fogel et al 1966), each individual produces one oospring and the best half from the parent and oospring populations are selected to form the new population. This is an overlapping system as the parents and their oospring constantly compete with each other for survival. In evolution strategies (Schwefel 1981), the (+) and the (;) models correspond to the …