The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem
Computers and Operations Research
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands
Computers and Operations Research
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
A memetic algorithm for extending wireless sensor network lifetime
Information Sciences: an International Journal
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework
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
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Memetic Algorithm for VLSI Floorplanning
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
| Hi-index | 0.07 |
Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimisation. That implies the evolutionary algorithm should be focused in exploring the search space while the local search method exploits the achieved solutions. To tackle this issue, we propose to maintain a higher diversity in the evolutionary algorithm's population by including a niching strategy in the memetic algorithm framework. In this work, we design a novel niching strategy where the niches divide the search space into hypercubes of equal size called regions forbidding the presence of two solutions in each region. The objective is to avoid the competition between the local search and the evolutionary algorithm. We tested this niching strategy in a memetic algorithm with local search chaining and obtained significant improvements. The resulting model also appeared to be very competitive with state-of-the-art algorithms.