An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving artificial intelligence
Evolving artificial intelligence
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
The Symbiotic Evolution of Solutions and Their Representations
Proceedings of the 6th International Conference on Genetic Algorithms
Co-evolving Memetic Algorithms: Initial Investigations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
Self Generating Metaheuristics in Bioinformatics: The Proteins Structure Comparison Case
Genetic Programming and Evolvable Machines
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
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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 arange of combinatorial optimisation problems show that algorithms embodying this mechanism locate the global optimum more reliably, without a significant computational overhead compared to the simpler schemes.