Computers and Operations Research
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
A new iterated local search algorithm using genetic crossover for the traveling salesman problem
Proceedings of the 1999 ACM symposium on Applied computing
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computers and Operations Research
A local genetic algorithm for binary-coded problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Variable Neighbourhood Search is a metaheuristic combining three components: generation, improvement, and shaking components. In this paper, we design a continuous Variable Neighbourhood Search algorithm based on three specialised Evolutionary Algorithms, which play the role of each aforementioned component: 1) an EA specialised in generating a good starting point as generation component, 2) an EA specialised in exploiting local information as improvement component, 3) and another EA specialised in providing local diversity as shaking component. Experiments are carried out on the noisy Black-Box Optimisation Benchmark 2009 testbed.