Ockham's Razor in memetic computing: Three stage optimal memetic exploration
Information Sciences: an International Journal
Memetic search for the max-bisection problem
Computers and Operations Research
A memetic algorithm for community detection in complex networks
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Information Sciences: an International Journal
An improved memetic algorithm for the antibandwidth problem
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Hybridizing evolutionary algorithms with opportunistic local search
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Adapt-MEMPSODE: a memetic algorithm with adaptive selection of local searches
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Compact Particle Swarm Optimization
Information Sciences: an International Journal
Power law-based local search in differential evolution
International Journal of Computational Intelligence Studies
Focusing the search: a progressively shrinking memetic computing framework
International Journal of Innovative Computing and Applications
A tabu search based memetic algorithm for the maximum diversity problem
Engineering Applications of Artificial Intelligence
A memetic algorithm for the Minimum Sum Coloring Problem
Computers and Operations Research
A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming
Applied Soft Computing
A memetic algorithm for the capacitated m-ring-star problem
Applied Intelligence
Causally-guided evolutionary optimization and its application to antenna array design
Integrated Computer-Aided Engineering
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Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems. The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes. Handbook of Memetic Algorithms organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now. A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem, memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, a great attention has been given by the editors to make it a compact and smooth work which covers all the main areas of computational intelligence optimization. It is not only a necessary read for researchers working in the research area, but also a useful handbook for practitioners and engineers who need to address real-world optimization problems. In addition, the book structure makes it an interesting work also for graduate students and researchers is related fields of mathematics and computer science.