Co-evolving memetic algorithms: a learning approach to robust scalable optimisation

This paper presents and examines the behaviour of a system whereby the rules governing local search within a memetic algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and then describe the implementation of a simple version in which the evolving rules are encoded as (condition:action) patterns applied to problem representation. We investigate the behaviour of the algorithm on a suite of test problems, and show considerable performance improvements over a simple genetic algorithm, a memetic algorithm using a fixed neighbourhood function, and a similar memetic algorithm which uses random rules, i.e. with the learning mechanisms disabled. Analysis of these results enables us to draw some conclusions about the way that even the simplified system is able to discover and exploit certain forms of structure and regularities if these exist within the problem space. We show that this "meta-learning" of problem features provides a means of creating highly scalable algorithms for some types of problems. We further demonstrate that in the absence of this kind of exploitable patterns, the use of continually evolving neighbourhood functions for the local search operators adds robustness to the memetic algorithm in a manner similar to variable neighbourhood search. Finally we draw some initial conclusions about the way in which this meta-learning takes place, via examination of the use of different pivot rules and pairing strategies between the population of solution and the population of rules.