In the preceding chapters we described the main varieties of evolutionary algorithms and described various examples of how they might be suitably implemented for different applications. In this chapter we turn our attention to systems in which, rather than existing as
stand-alone algorithms, EA-based approaches are either incorporated within larger systems, or alternatively have other
methods or data structures incorporated within them.
This category of algorithms is very successful in practice and forms a rapidly growing research area with great potential. This area and the
algorithms that form its subject of study are named memetic algorithms (MA).
In this chapter we explain the rationale behind MAs, outline a number of possibilities for combining EAs with other techniques, and give
some guidelines for designing successful hybrid algorithms.
Contents:
10.1 Motivation for Hybridising EAs ………………………167
10.2 A Brief Introduction to Local Search…………………..170
10.2.1 Lamarckianism and the Baldwin Effect……………171
10.3 Structure of a Memetic Algorithm …………………….172
10.3.1 Heuristic or Intelligent Initialisation ……………..172
10.3.2 Hybridisation Within Variation Operators: Intelligent Crossover and Mutation……………………….174
10.3.3 Local Search Acting on the Output from Variation Operators ………………………………….175
10.3.4 Hybridisation During Genotype to Phenotype Mapping 176
10.4 Adaptive Memetic Algorithms ……………………….177
10.5 Design Issues for Memetic Algorithms………………….179
10.6 Example Application: Multistage Memetic Timetabling . . . . . . . 181