Credit assignment in adaptive memetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
User-centric image segmentation using an interactive parameter adaptation tool
Pattern Recognition
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Modelling and Simulation in Engineering
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Natural Computing: an international journal
Disturbed Exploitation compact Differential Evolution for limited memory optimization problems
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Index-based genetic algorithm for continuous optimization problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Differential evolution with self adaptive local search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Ockham's Razor in memetic computing: Three stage optimal memetic exploration
Information Sciences: an International Journal
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
COW: a co-evolving memetic wrapper for herb-herb interaction analysis in TCM informatics
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
HyFlex: a benchmark framework for cross-domain heuristic search
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
Information Sciences: an International Journal
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
An adaptive evolutionary approach for real-time vehicle routing and dispatching
Computers and Operations Research
An intelligent multi-restart memetic algorithm for box constrained global optimisation
Evolutionary Computation
An analysis on separability for Memetic Computing automatic design
Information Sciences: an International Journal
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Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed