Credit assignment in adaptive memetic algorithms

Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta-heuristics which maintain a set of local search operators applying them at different stages of the search. This creates a need to make decisions about which operator to use when. Several different schemes have been proposed, but most of them assume there is a fixed set of predefined operators. This makes them unsuitable for use within the broader context of adaptive learning systems where the set of available operators can change over time. Here we investigate a range of different schemes, and propose a novel method for estimating an operator’s current utility, which is shown to avoid some of the problems of noise inherent in simpler schemes. Results on a range of combinatorial optimisation problems show that algorithms embodying this mechanism locate the global optimum more reliably, without a signi�...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: J. E. Smith
• Keywords: Different Schemes, GECCO 2007, Local Search, Memetic Algorithms