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
SAW-ing EAs: adapting the fitness function for solving constrained problems
New ideas in optimization
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
On Appropriate Adaptation Levels for the Learning of Gene Linkage
Genetic Programming and Evolvable Machines
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Co-evolving Memetic Algorithms: Initial Investigations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Strategy Parameter Variety In Self-adaptation Of Mutation Rates
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
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
Credit assignment in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Credit assignment in adaptive memetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
Extreme Value Based Adaptive Operator Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
AMA: a new approach for solving constrained real-valued optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Super-fit control adaptation in memetic differential evolution frameworks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Adaptive cellular memetic algorithms
Evolutionary Computation
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Research frontier: linkage discovery through data mining
IEEE Computational Intelligence Magazine
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
The Self-Organization of Interaction Networks for Nature-Inspired Optimization
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
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
A Multi-Facet Survey on Memetic Computation
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
An analysis on separability for Memetic Computing automatic design
Information Sciences: an International Journal
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Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes outperform global reward schemes in combinatorial spaces, unlike in continuous spaces. An analysis of evolving meme behaviour is used to explain these findings.