Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Tabu Search
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Proceedings of the 3rd International Conference on Genetic Algorithms
Empirical Analysis of the Factors that Affect the Baldwin Effect
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
A distance-based selection of parents in genetic algorithms
Metaheuristics
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
A tabu search algorithm for the quadratic assignment problem
Computational Optimization and Applications
An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Genetic subgradient method for solving location-allocation problems
Applied Soft Computing
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Computers and Operations Research
Journal of Global Optimization
A hybrid genetic algorithm for the multi-depot vehicle routing problem
Engineering Applications of Artificial Intelligence
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Self Controlling Tabu Search algorithm for the Quadratic Assignment Problem
Computers and Industrial Engineering
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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A differential improvement modification to Hybrid Genetic Algorithms is proposed. The general idea is to perform more extensive improvement algorithms on higher quality solutions. Our proposed Differential Improvement (DI) approach is of rather general character. It can be implemented in many different ways. The paradigm remains invariant and can be easily applied to a wider class of optimization problems. Moreover, the DI framework can also be used within other Hybrid metaheuristics like Hybrid Scatter Search algorithms, Particle Swarm Optimization, or Bee Colony Optimization techniques. Extensive experiments show that the new approach enables to improve significantly the performance of Hybrid Genetic Algorithms without adding extra computer time. Additional experiments investigated the trade-off between the number of generations and the number of iterations of the improvement algorithm. These experiments yielded six new best known solutions to benchmark quadratic assignment problems. Many other variants of the proposed algorithm are suggested for future research.