Neural Computation
A memetic algorithm for multi-objective dynamic location problems
Journal of Global Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
User-centric evolutionary computation is an optimization paradigm that tries to integrate the human user and the evolutionary algorithm in a smooth way, favoring bi-directional communication and establishing synergies among these two actors. We explore the possibilities for such an approach in the context of memetic algorithms, with application to the travelling salesman problem. Some ways to canalize this cooperation via the introduction of dynamic constraints and selective local search are hinted, and implementation and interfacing issues are discussed. The reported experiments on TSPLIB instances provide encouraging results for these techniques.