Introduction
Memetic Algorithms (MAs) are a fairly recent breed of optimization algorithms created through a synergetic coupling of global and local search strategies [615].While predecessors of MAs, i.e. Genetic Algorithms (GAs) and Evolutionary Algorithms (EAs) have had significant success in solving a number of real life complex optimization problems in the past, their performance can be greatly improved though a hybridization with other techniques [188]. GAs or EAs hybridized with local search strategies are commonly referred as memetic algorithms. These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. While, the underlying GA/EA provides the ability for exploration, the local search aids in exploitation [492]. The exploitation schemes adopted in MAs include incorporation of heuristics, approximation algorithms, local search algorithms, specialized schemes for recombination etc.
Keywords
- Local Search
- Partial Reordering
- Constrain Optimization Problem
- Sequential Quadratic Program
- Memetic Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2012 Springer-Verlag Berlin Heidelberg
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Ray, T., Sarker, R. (2012). Memetic Algorithms in Constrained Optimization. In: Neri, F., Cotta, C., Moscato, P. (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_9
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DOI: https://doi.org/10.1007/978-3-642-23247-3_9
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