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In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run—so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems and a hard problem from operations research, the flowshop sequencing problem, are therefore examined. The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled.

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The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such a combination should outperform the single heuristics. This article presents a GA-based method that produces general hyper-heuristics that solve two-dimensional regular (rectangular) and irregular (convex polygonal) bin-packing problems. A hyper-heuristic is used to define a highlevel heuristic that controls low-level heuristics. The hyper-heuristic should decide when and where to apply each single low-level heuristic, depending on the given problem state. In this investigation two kinds of heuristics were considered: for selecting the figures (pieces) and objects (bins), and for placing the figures into the objects. Some of the heuristics were taken from the literature, others were adapted, and some other variations developed by us. We chose the most representative heuristics of their type, considering their individual performance in various studies and also in an initial experimentation on a collection of benchmark problems. The GA included in the proposed model uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyperheuristics, when tested with a large set of benchmark problems, produce outstanding results for most of the cases. The testbed is composed of problems used in other similar studies in the literature. Some additional instances for the testbed were randomly generated.

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This paper describes an artificial immune system (AIS) approach to producing robust schedules for a dynamic jobshop scheduling problem in which jobs arrive continually, and the environment is subject to change due to practical reasons. We investigate whether an AIS can be evolved using a genetic algorithm, (GA), and then used to produce sets of schedules which together cover a range of contingencies, both foreseeable and unforeseeable. We compare the quality of the schedules to those produced using a genetic algorithm specifically designed for tackling job-shop scheduling problems, and find that the schedules produced from the evolved AIS compare favourably to those produced by the GA. Furthermore, we find that the AZS schedules are robust in that there are large similarities between each schedule in the set, indicating that a switch from one schedule to another could be performed with minimal disruption if rescheduling is required. 0-7803-4869-9/98 $10.0001998 IEEE